Add files using upload-large-folder tool

.gitattributes

@@ -58,3 +58,15 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+movielens_32m/ml-32m/ratings.csv filter=lfs diff=lfs merge=lfs -text
+avazu/train.csv filter=lfs diff=lfs merge=lfs -text
+foursquare/dataset_TSMC2014_NYC.csv filter=lfs diff=lfs merge=lfs -text
+foursquare/dataset_TSMC2014_TKY.csv filter=lfs diff=lfs merge=lfs -text
+yoochoose/yoochoose-buys.dat filter=lfs diff=lfs merge=lfs -text
+yoochoose/yoochoose-test.dat filter=lfs diff=lfs merge=lfs -text
+retailrocket/events.csv filter=lfs diff=lfs merge=lfs -text
+amazon_m2/sessions_test_task1.csv filter=lfs diff=lfs merge=lfs -text
+amazon_m2/sessions_train.csv filter=lfs diff=lfs merge=lfs -text
+lastfm_360k/usersha1-profile.tsv filter=lfs diff=lfs merge=lfs -text
+retailrocket/item_properties_part2.csv filter=lfs diff=lfs merge=lfs -text
+bookcrossing/Ratings.csv filter=lfs diff=lfs merge=lfs -text

DATASETS.md

@@ -0,0 +1,252 @@
+# Dataset Catalog
+
+所有 raw data 存於 `/home/sadoo/Projects/kvcache/research_data/raw/`。本檔記錄每個 dataset 的 **來源 / 下載方式 / 規格 / schema**，方便未來重下載 or 引用 paper。
+
+更新日期：2026-04-19
+
+---
+
+## A. Paper-Reference Datasets (MaxEmbed ASPLOS'24 baseline)
+
+### 1. Criteo (Display Advertising Challenge)
+- **Path**: `raw/criteo_kaggle/`（17GB）
+- **Source**:
+  - Original: Criteo Labs 2014 Kaggle Display Advertising Challenge
+  - Files: `train.txt` (11GB, 45.8M rows) + `test.txt` (1.4GB) + `dac.tar.gz` (4.3GB archive)
+- **Download**: Kaggle API `kaggle competitions download -c criteo-display-ad-challenge`（現在需 Kaggle 授權；已備份）
+- **Schema**：TSV (`\t` 分隔) — `label \t 13×int \t 26×hex_category`（40 columns）
+- **Paper spec**: 35M items, 45.8M queries
+- **Our stats**: 33.76M unique items, **45.84M queries** ✓
+
+### 2. Criteo Terabyte (1TB Click Logs)
+- **Path**: `raw/criteo_terabyte/`（39GB compressed gz，22/24 天）
+- **Source**:
+  - Original: `https://storage.googleapis.com/criteo-cail-datasets/day_{0..23}.gz`
+  - Script: `dlrm/torchrec_dlrm/scripts/download_Criteo_1TB_Click_Logs_dataset.sh`
+- **Status**: **缺 day_0 / day_1**（其餘 22 天齊全，足夠做 drift 實驗）
+- **Schema**：同 Criteo（40 cols TSV）
+- **Paper spec**: 882M items, 4.37B queries (full 24 days)
+- **Supplementary subset**: `raw/criteo_terabyte/merci_day_0_subset/` — MERCI 切出的 7 slices (~230MB)
+- **Note**: 若要 full paper scale 需要補 day_0/1（~90GB uncompressed）
+
+### 3. Avazu
+- **Path**: `raw/avazu/`（7.1GB）
+- **Source**: Kaggle Avazu CTR Prediction Competition 2015
+- **Download**: `kaggle competitions download -c avazu-ctr-prediction`
+- **Files**: `train.csv` (5.9GB) + `avazu-ctr-train.zip` (1.3GB backup)
+- **Schema**: CSV with header — `id, click, hour, C1, banner_pos, site_id, site_domain, site_category, app_id, ..., C21` (24 cols)
+- **Paper spec**: 9.45M items, 40.4M queries
+- **Note**: 需處理到 paper scale（現有 MERCI filtered 版只 1.07M items）
+
+### 4. Amazon M2 (KDD Cup 2023)
+- **Path**: `raw/amazon_m2/`（832MB）
+- **Source**:
+  - Official: https://www.aicrowd.com/challenges/amazon-kdd-cup-23-multilingual-recommendation-challenge（需 login）
+  - **Used mirror**: Kaggle `riseserise/kdd-cup-2023`
+  - Alternative code repo: `github.com/HaitaoMao/Amazon-M2-Session-Recommendation`（code only, no data）
+- **Download**: `kaggle datasets download -d riseserise/kdd-cup-2023 --unzip`
+- **Files**:
+  - `sessions_train.csv` (248MB, 3.6M sessions, 6 locales: UK/DE/JP/IT/FR/ES)
+  - `sessions_test_task1/2/3.csv`
+  - `products_train.csv` (562MB, 1.55M product metadata rows)
+- **Schema (sessions_train)**: CSV with multi-line quoted fields — `prev_items (numpy repr ['A' 'B'] format), next_item, locale`
+- **Paper spec**: 1.39M items, 3.6M queries
+- **Our stats**: **1,393,956 items, 3,606,249 sessions** ✓ MATCH
+- **Processed**: `processed/amazon_m2/amazon_m2_session_remapped.txt`（已跑 `convert_amazon_m2.py` + MaxEmbed `process.py`）
+- **Paper arxiv**: https://arxiv.org/abs/2307.09688
+
+### 5. Alibaba-iFashion (POG KDD'19)
+- **Path**: `raw/alibaba_ifashion/`（23GB raw / 9.2GB archived）
+- **Source**:
+  - Official: `github.com/wenyuer/POG`（Personalized Outfit Generation, Alibaba iFashion）
+  - **Download**: Google Drive folder `1xFdx5xuNXHGsUVG2VIohFTXf9S7G5veq` via `gdown`
+  - Papers with Code: https://paperswithcode.com/dataset/ifashion-alibaba-pog
+- **Files**:
+  - `item_data.txt` (1.17GB) — 5.12M rows, 4.75M unique items, 75 categories
+  - `outfit_data.txt` (155MB) — 1.01M outfits (each = session-like query)
+  - `user_data.txt` (21.75GB) — 19.19M user-click sessions
+- **Schema**:
+  - `item_data.txt`: CSV — `item_id, cate_id, pic_url, title`
+  - `outfit_data.txt`: CSV — `outfit_id, item_id;item_id;...` (semicolon list)
+  - `user_data.txt`: CSV — `user_id, item_id;item_id;..., outfit_id`
+- **Paper spec**: 4.46M items, 999K queries
+- **Our stats**: 4.75M items raw → **4.46M after freq filter**, 1.01M outfits ≈ **999K** ✓
+- **Note**: Paper 53.6GB 差額是 POG visual embeddings / images，不在此 text-only 版
+- **Paper arxiv**: https://arxiv.org/abs/1905.01866
+
+---
+
+## B. Classic Recommendation Datasets
+
+### 6. Taobao User Behavior
+- **Path**: `raw/taobao/`（4.4GB）
+- **Source**: Alibaba Tianchi — https://tianchi.aliyun.com/dataset/649
+- **Files**: `UserBehavior.csv` (3.5GB) + `userbehavior.zip` (906MB backup)
+- **Schema**: CSV — `user_id, item_id, category_id, behavior_type, timestamp`（behavior: pv/buy/cart/fav）
+- **Stats**: 100M events, ~1M users, ~4.1M items
+
+### 7. Amazon Reviews (McAuley UCSD)
+- **Path**: `raw/amazon/`（46GB）
+- **Source**: https://cseweb.ucsd.edu/~jmcauley/datasets.html
+- **Files**:
+  - `Electronics.json.gz` (473MB) + `meta_Electronics.json.gz` (178MB)
+  - `Home_and_Kitchen.json.gz` (132MB) + meta (146MB)
+  - `Office_Products.json.gz` (18MB) + meta (46MB)
+  - **`All_Amazon_Meta.json`** (12GB uncompressed) — complete Amazon product metadata
+  - **`All_Amazon_Review.json.gz`** (34GB) — all review JSON
+
+### 8. MovieLens (GroupLens)
+- **Source**: https://files.grouplens.org/datasets/movielens/
+- **Paths / Variants**:
+  - `raw/movielens_1m/`: 24MB — 1M ratings, 6,040 users, 3,883 movies — `ratings.dat` `::` delimited
+  - `raw/movielens_10m/`: 257MB — 10M ratings, ~72K users, 10,681 movies
+  - `raw/movielens_25m/`: 1.1GB — **25M ratings, 162,541 users, 62,423 movies**（主要 variant）
+  - `raw/movielens_32m/`: 912MB — 32M ratings, ~200K users, 87,585 movies（最新）
+- **Schema (25M)**: CSV — `userId, movieId, rating, timestamp`（ratings 0.5-5.0，0.5 step，Unix ts）
+- **Extras (25M)**: `genome-scores.csv`, `genome-tags.csv`, `links.csv`, `tags.csv`
+
+### 9. Yelp Open Dataset
+- **Path**: `raw/yelp/`（8.7GB）
+- **Source**: https://www.yelp.com/dataset（需 signup）；**used mirror**: Kaggle `yelp-dataset/yelp-dataset` (v2022)
+- **Download**: `kaggle datasets download -d yelp-dataset/yelp-dataset --unzip`
+- **Files** (newline-delimited JSON):
+  - `review.json` (5.3GB, **6,990,280 reviews**)
+  - `user.json` (3.4GB, 1.99M users)
+  - `business.json` (119MB, 150,346 businesses)
+  - `checkin.json` (287MB)
+  - `tip.json` (181MB)
+- **Schema (review)**: `{review_id, user_id, business_id, stars, useful, funny, cool, text, date}`
+
+### 10. LastFM
+- **LastFM-1K**: `raw/lastfm_1k/` 2.4GB
+  - **Source**: Ocelma Music Recommendation (原站 403)；**used mirror**: Kaggle `japarra27/lastfm-dataset`
+  - 19.15M listening records, 992 users, 177K artists
+  - Schema: TSV — `user_id, timestamp, artist_id, artist_name, track_id, track_name`
+- **LastFM-360K**: `raw/lastfm_360k/` 1.6GB
+  - Mirror: Kaggle `dhaatrisanisetty/last-fm`
+  - 17.56M records, 359,347 users, ~295K artists
+  - Schema: TSV — `user_sha1, artist_mbid, artist_name, plays`
+
+### 11. Gowalla (Location Check-ins)
+- **Path**: `raw/gowalla/`（398MB）
+- **Source**: Stanford SNAP — https://snap.stanford.edu/data/loc-Gowalla.html
+- **Files**:
+  - `loc-gowalla_totalCheckins.txt` — 6.44M check-ins
+  - `loc-gowalla_edges.txt` — 1.9M social edges
+- **Schema (check-ins)**: TSV — `user_id, timestamp, lat, lng, location_id`
+
+### 12. BookCrossing
+- **Path**: `raw/bookcrossing/`（103MB）
+- **Source**: http://www2.informatik.uni-freiburg.de/~cziegler/BX/（原站）；Kaggle mirror `arashnic/book-recommendation-dataset`
+- **Files**: `Ratings.csv` (1.15M), `Users.csv` (278,859), `Books.csv` (271,360)
+- **Schema (ratings)**: CSV — `User-ID, ISBN, Book-Rating`(0-10)
+
+### 13. Foursquare (NYC + Tokyo)
+- **Path**: `raw/foursquare/`（98MB）
+- **Source**: https://sites.google.com/site/yangdingqi/home/foursquare-dataset（TSMC2014）；Kaggle mirror `chetanism/foursquare-nyc-and-tokyo-checkin-dataset`
+- **Files**:
+  - `dataset_TSMC2014_NYC.csv` (227,428 check-ins)
+  - `dataset_TSMC2014_TKY.csv` (573,703 check-ins)
+- **Schema**: CSV — `userId, venueId, venueCategoryId, venueCategory, latitude, longitude, timezoneOffset, utcTimestamp`
+
+### 14. Netflix Prize
+- **Path**: `raw/netflix/`（2GB）
+- **Source**: Netflix Prize dataset (2006)；Kaggle `netflix-inc/netflix-prize-data`
+- **Files**: `combined_data_1.txt` + `_2` + `_3` + `_4` (100M ratings total) + `probe.txt` + `qualifying.txt` + `movie_titles.csv`
+- **Schema**: 每個 movie 以 `<movie_id>:` header 開始，之後 `user_id, rating (1-5), date` 每行
+
+### 15. Steam Reviews
+- **Path**: `raw/steam/`（2.1GB）
+- **Source**: McAuley UCSD (404 for both URLs)；**used mirrors**: Kaggle `andrewmvd/steam-reviews` + `tamber/steam-video-games`
+- **Files**:
+  - `dataset.csv` — 6.4M reviews (columns: `app_id, app_name, review_text, review_score, review_votes`)
+  - `steam-200k.csv` — 200K user-game events (`user_id, game, purchase/play, hours, 0`)
+
+### 16. JD (JData 2016)
+- **Path**: `raw/jd/`（2.2GB）
+- **Source**: JD.com JData 2016 Competition（原站需中國手機驗證）；**used mirror**: Kaggle `owincontext/jdata2016`
+- **Files**: 3 個 action files (Feb/Mar/Apr = 11.5+25.9+13.2M rows) + User (105K) + Product (24K) + Comment (558K)
+- **Schema (action)**: CSV — `user_id, sku_id, time, model_id, type, cate, brand`（type: 1=browse, 2=add-cart, 3=delete, 4=purchase, 5=favorite, 6=click）
+
+### 17. Tmall (IJCAI16)
+- **Path**: `raw/tmall/`（1.7GB）
+- **Source**: Alibaba Tianchi（需 Aliyun login）；**used mirror**: Kaggle `galuhramaditya/tmall-ijcai16`
+- **Files**: 44.5M interactions
+- **Schema**: CSV — `user_id, seller_id, item_id, category_id, timestamp, interaction`（click/purchase）
+
+### 18. Anime Recommendations
+- **Path**: `raw/anime/`（108MB）
+- **Source**: Kaggle `CooperUnion/anime-recommendations-database`
+- **Files**: `anime.csv` (12.3K anime) + `rating.csv` (7.8M ratings)
+- **Schema**:
+  - anime: `anime_id, name, genre, type, episodes, rating, members`
+  - rating: `user_id, anime_id, rating`（-1 = unrated）
+
+### 19. MIND (Microsoft News Recommendation)
+- **Path**: `raw/mind/`（153MB）
+- **Source**: https://msnews.github.io/（原站 Azure storage 已 disabled）；**used mirror**: Kaggle `arashnic/mind-news-dataset`
+- **Files** (MINDsmall_train only, no dev/test):
+  - `behaviors.tsv` — 157K impressions — `impr_id, user, time, history, impressions (Nxxx-1/0)`
+  - `news.tsv` — 51K news — `news_id, cat, subcat, title, abstract, url, entities, abstract_entities`
+  - `entity_embedding.vec`, `relation_embedding.vec`
+
+---
+
+## C. Supplementary / Intermediate
+
+### 20. MERCI day_0_subset
+- **Path**: `raw/criteo_terabyte/merci_day_0_subset/`
+- **Purpose**: 由 MERCI 從 Criteo Terabyte day_0 切出的 7 slices（142858 × 7 samples），用於 MERCI pipeline benchmark
+- **Note**: 雖放在 criteo_terabyte/ 下，實際只來自 day_0，約 day_0 的 1M-subset
+
+### 21. MERCI _1_raw / _downloads (archived)
+- **Path**: `raw/_merci_1_raw/`, `raw/_merci_downloads/`
+- **Purpose**: MERCI 原 pipeline 前期下載的 raw stage（現已遷移）
+
+---
+
+## D. Download Method 統整
+
+| Method | Used for |
+|--------|----------|
+| `kaggle datasets download -d <ref> --unzip` | Amazon M2, Yelp, Netflix, Steam, JD, Tmall, Anime, MIND, BookCrossing, Foursquare, LastFM |
+| `wget`（直接 HTTP） | Criteo Terabyte, MovieLens（GroupLens 主站仍穩定）, Gowalla（SNAP） |
+| `gdown`（Google Drive） | Alibaba-iFashion POG |
+| Kaggle Competitions (`kaggle competitions download -c`) | Criteo, Avazu（現需授權） |
+
+**斷鏈提醒**：
+- Kaggle API 需要 `~/.kaggle/kaggle.json` token（現狀可能 401）
+- Criteo Terabyte Google Cloud Storage URL（公開，穩定）
+- McAuley UCSD（Steam / Amazon reviews）近期連線不穩 → 已改用 Kaggle mirror
+- Microsoft MIND Azure storage 停止公開 → Kaggle mirror
+
+---
+
+## E. 使用規範
+
+1. **Read-only**：`raw/` 下載後建議 `chmod -R 444` 防誤改
+2. **單一副本**：不複製 raw 到 processed/，統一由 `processed/{dataset}/` 引用
+3. **引用**：paper 寫作時請標註 paper 來源與 download mirror（reviewers 會檢查 reproducibility）
+
+## F. 空間統計（2026-04-19）
+
+```
+raw/ 總計 150GB
+├── criteo_terabyte/    39GB
+├── criteo_kaggle/      17GB
+├── alibaba_ifashion/   23GB
+├── avazu/              7.1GB
+├── taobao/             4.4GB
+├── yelp/               8.7GB
+├── lastfm_1k/          2.4GB  ├── lastfm_360k/         1.6GB
+├── movielens_25m/      1.1GB  ├── movielens_32m/       912MB
+├── movielens_10m/      257MB  ├── movielens_1m/        24MB
+├── amazon/             991MB  (+ All_Amazon_{Meta,Review} 46GB)
+├── amazon_m2/          832MB
+├── netflix/            2.0GB
+├── steam/              2.1GB  ├── jd/                  2.2GB
+├── tmall/              1.7GB  ├── anime/               108MB
+├── mind/               153MB  ├── gowalla/             398MB
+├── bookcrossing/       103MB  ├── foursquare/          98MB
+└── _merci_{1_raw,downloads}/  (archive)
+```

_merci_downloads/download.log

@@ -0,0 +1,29 @@
+=== Download started: Sat Mar 21 03:44:30 AM CST 2026 ===
+
+[1/3] Downloading Avazu...
+401 Client Error: Unauthorized for url: https://api.kaggle.com/v1/competitions.CompetitionApiService/DownloadDataFiles
+Kaggle download failed, trying direct URL...
+--2026-03-21 03:44:31--  https://www.kaggle.com/competitions/avazu-ctr-prediction/data
+Resolving www.kaggle.com (www.kaggle.com)... 35.244.233.98
+Connecting to www.kaggle.com (www.kaggle.com)|35.244.233.98|:443... connected.
+HTTP request sent, awaiting response... 200 OK
+Length: unspecified [text/html]
+Saving to: ‘avazu.zip’
+
+     0K .....                                                  1.06M=0.005s
+
+2026-03-21 03:44:31 (1.06 MB/s) - ‘avazu.zip’ saved [5525]
+
+Unzipping Avazu...
+Archive:  avazu.zip
+  End-of-central-directory signature not found.  Either this file is not
+  a zipfile, or it constitutes one disk of a multi-part archive.  In the
+  latter case the central directory and zipfile comment will be found on
+  the last disk(s) of this archive.
+unzip:  cannot find zipfile directory in one of avazu.zip or
+        avazu.zip.zip, and cannot find avazu.zip.ZIP, period.
+ one disk of a multi-part archive.  In the
+  latter case the central directory and zipfile comment will be found on
+  the last disk(s) of this archive.
+unzip:  cannot find zipfile directory in one of avazu.zip or
+        avazu.zip.zip, and cannot find avazu.zip.ZIP, period.

_merci_downloads/download_all.sh

@@ -0,0 +1,59 @@
+#!/bin/bash
+# Download Avazu, Taobao, Criteo Kaggle datasets
+# Run in background: nohup bash download_all.sh > download.log 2>&1 &
+
+set -e
+LOG="/home/sadoo/MERCI/data/raw_downloads/download.log"
+exec > >(tee -a "$LOG") 2>&1
+
+echo "=== Download started: $(date) ==="
+echo ""
+
+# ── 1. Avazu (Kaggle, ~260MB) ──
+echo "[1/3] Downloading Avazu..."
+cd /home/sadoo/MERCI/data/raw_downloads/avazu
+kaggle competitions download -c avazu-ctr-prediction 2>&1 || {
+    echo "Kaggle download failed, trying direct URL..."
+    wget -c "https://www.kaggle.com/competitions/avazu-ctr-prediction/data" -O avazu.zip 2>&1 || echo "Avazu download failed"
+}
+if ls *.zip 1>/dev/null 2>&1; then
+    echo "Unzipping Avazu..."
+    unzip -o *.zip
+    echo "Avazu done: $(du -sh .)"
+fi
+echo ""
+
+# ── 2. Criteo Kaggle (Kaggle, ~11GB) ──
+echo "[2/3] Downloading Criteo Kaggle..."
+cd /home/sadoo/MERCI/data/raw_downloads/criteo_kaggle
+kaggle competitions download -c criteo-display-ad-challenge 2>&1 || {
+    echo "Criteo Kaggle download failed"
+}
+if ls *.zip 1>/dev/null 2>&1; then
+    echo "Unzipping Criteo Kaggle..."
+    unzip -o *.zip
+    echo "Criteo Kaggle done: $(du -sh .)"
+fi
+echo ""
+
+# ── 3. Taobao UserBehavior (Alibaba, via tianchi or mirror) ──
+echo "[3/3] Downloading Taobao UserBehavior..."
+cd /home/sadoo/MERCI/data/raw_downloads/taobao
+# Taobao UserBehavior is on Alibaba Tianchi platform
+# Direct download may require login; try kaggle mirror first
+kaggle datasets download -d pengxu-pku/taobao-userbehavior 2>&1 || {
+    echo "Kaggle mirror failed. Trying alternative sources..."
+    kaggle datasets download -d vishalsinghprojects/taobao-user-behaviour 2>&1 || {
+        echo "Taobao download failed - may need manual download from tianchi.aliyun.com"
+    }
+}
+if ls *.zip 1>/dev/null 2>&1; then
+    echo "Unzipping Taobao..."
+    unzip -o *.zip
+    echo "Taobao done: $(du -sh .)"
+fi
+echo ""
+
+echo "=== Download complete: $(date) ==="
+echo "=== Disk usage ==="
+du -sh /home/sadoo/MERCI/data/raw_downloads/*/

amazon_m2/sessions_test_task1.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d3806ce1bdf2866add188ccddb212941c3a4e004dbbdab6c2023f5b2347c2d2d
+size 19368014

amazon_m2/sessions_train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83f9d5c72b7305243ceb1d06aaf96610a050e84d428ae55138e851e70e313954
+size 259228456

avazu/train.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:49f0ba4b970e189818f43be4bbd32389d1b3ba23240abf7f5d6958647994ff74
+size 6311147778

bookcrossing/Ratings.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fbb819408aa8d42e7675200b26d48d66660b6e712bbd82585e5b564053d29de9
+size 22633892

criteo_kaggle/dac/readme.txt

@@ -0,0 +1,53 @@
+        ------ Display Advertising Challenge ------
+
+Dataset: dac-v1
+
+This dataset contains feature values and click feedback for millions of display 
+ads. Its purpose is to benchmark algorithms for clickthrough rate (CTR) prediction.
+It has been used for the Display Advertising Challenge hosted by Kaggle:
+https://www.kaggle.com/c/criteo-display-ad-challenge/
+
+===================================================
+
+Full description:
+
+This dataset contains 2 files:
+  train.txt
+  test.txt
+corresponding to the training and test parts of the data. 
+
+====================================================
+
+Dataset construction:
+
+The training dataset consists of a portion of Criteo's traffic over a period
+of 7 days. Each row corresponds to a display ad served by Criteo and the first
+column is indicates whether this ad has been clicked or not.
+The positive (clicked) and negatives (non-clicked) examples have both been
+subsampled (but at different rates) in order to reduce the dataset size.
+
+There are 13 features taking integer values (mostly count features) and 26
+categorical features. The values of the categorical features have been hashed
+onto 32 bits for anonymization purposes. 
+The semantic of these features is undisclosed. Some features may have missing values.
+
+The rows are chronologically ordered.
+
+The test set is computed in the same way as the training set but it 
+corresponds to events on the day following the training period. 
+The first column (label) has been removed.
+
+====================================================
+
+Format:
+
+The columns are tab separeted with the following schema:
+<label> <integer feature 1> ... <integer feature 13> <categorical feature 1> ... <categorical feature 26>
+
+When a value is missing, the field is just empty.
+There is no label field in the test set.
+
+====================================================
+
+Dataset assembled by Olivier Chapelle (o.chapelle@criteo.com)
+

criteo_kaggle/readme.txt

@@ -0,0 +1,53 @@
+        ------ Display Advertising Challenge ------
+
+Dataset: dac-v1
+
+This dataset contains feature values and click feedback for millions of display 
+ads. Its purpose is to benchmark algorithms for clickthrough rate (CTR) prediction.
+It has been used for the Display Advertising Challenge hosted by Kaggle:
+https://www.kaggle.com/c/criteo-display-ad-challenge/
+
+===================================================
+
+Full description:
+
+This dataset contains 2 files:
+  train.txt
+  test.txt
+corresponding to the training and test parts of the data. 
+
+====================================================
+
+Dataset construction:
+
+The training dataset consists of a portion of Criteo's traffic over a period
+of 7 days. Each row corresponds to a display ad served by Criteo and the first
+column is indicates whether this ad has been clicked or not.
+The positive (clicked) and negatives (non-clicked) examples have both been
+subsampled (but at different rates) in order to reduce the dataset size.
+
+There are 13 features taking integer values (mostly count features) and 26
+categorical features. The values of the categorical features have been hashed
+onto 32 bits for anonymization purposes. 
+The semantic of these features is undisclosed. Some features may have missing values.
+
+The rows are chronologically ordered.
+
+The test set is computed in the same way as the training set but it 
+corresponds to events on the day following the training period. 
+The first column (label) has been removed.
+
+====================================================
+
+Format:
+
+The columns are tab separeted with the following schema:
+<label> <integer feature 1> ... <integer feature 13> <categorical feature 1> ... <categorical feature 26>
+
+When a value is missing, the field is just empty.
+There is no label field in the test set.
+
+====================================================
+
+Dataset assembled by Olivier Chapelle (o.chapelle@criteo.com)
+

foursquare/dataset_TSMC2014_NYC.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60a235386a5324f55c9f24fb1b06e55a38a0d53c0242435d754b38c0cc0b514a
+size 29253118

foursquare/dataset_TSMC2014_TKY.csv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:849365e52e970c85819b3cc1559bb00bedbe2e37f8e274d77241d62e89ac85f2
+size 73067343

lastfm_1k/userid-profile.tsv

@@ -0,0 +1,993 @@
+#id	gender	age	country	registered
+user_000001	m		Japan	Aug 13, 2006
+user_000002	f		Peru	Feb 24, 2006
+user_000003	m	22	United States	Oct 30, 2005
+user_000004	f			Apr 26, 2006
+user_000005	m		Bulgaria	Jun 29, 2006
+user_000006		24	Russian Federation	May 18, 2006
+user_000007	f		United States	Jan 22, 2006
+user_000008	m	23	Slovakia	Sep 28, 2006
+user_000009	f	19	United States	Jan 13, 2007
+user_000010	m	19	Poland	May 4, 2006
+user_000011	m	21	Finland	Sep 8, 2005
+user_000012	f	28	United States	Mar 30, 2005
+user_000013	f	25	Romania	Sep 25, 2006
+user_000014				Jan 27, 2006
+user_000015		21	Cote D'Ivoire	Oct 3, 2006
+user_000016	m		United Kingdom	Aug 5, 2005
+user_000017	m	22	Morocco	Aug 27, 2007
+user_000018		22	United Kingdom	Aug 26, 2005
+user_000019	f	29	Mexico	Nov 10, 2005
+user_000020	f	27	Germany	Jul 24, 2006
+user_000021	m	27	Canada	Mar 16, 2005
+user_000022	m	38	United Kingdom	May 14, 2006
+user_000023	f		Germany	Aug 15, 2006
+user_000024	f	25	United States	Oct 3, 2004
+user_000025		19	Sweden	Nov 10, 2005
+user_000026	m	22	Turkey	Dec 21, 2005
+user_000027	m	24	United States	Jul 25, 2005
+user_000028	m	20	United Kingdom	Nov 19, 2005
+user_000029	m		United States	Feb 13, 2006
+user_000030				
+user_000031		38	United States	Aug 3, 2006
+user_000032	m	23	Italy	Feb 28, 2005
+user_000033	f	20	Poland	Dec 23, 2005
+user_000034	f	35	Brazil	Sep 15, 2005
+user_000035	m	22	Australia	Mar 7, 2005
+user_000036	f	21	Mexico	Feb 19, 2007
+user_000037	m		United States	Aug 13, 2007
+user_000038	m	29	Russian Federation	Jul 6, 2006
+user_000039	m	21	United Kingdom	Jun 4, 2006
+user_000040	f	21	Germany	Apr 21, 2006
+user_000041	m	30	Sweden	Jun 14, 2005
+user_000042	f	24	United States	Dec 29, 2005
+user_000043	f	26	United States	Mar 18, 2006
+user_000044	f	21	Italy	Feb 27, 2007
+user_000045	m	29	Chile	Jul 9, 2006
+user_000046	m	24	Greece	Apr 23, 2007
+user_000047	f	19	Croatia	Aug 26, 2006
+user_000048	m	33	Italy	Dec 29, 2006
+user_000049				Jan 11, 2006
+user_000050	f	24	United Kingdom	Feb 10, 2006
+user_000051	f	25	Brazil	Aug 10, 2006
+user_000052	m	20	Russian Federation	Oct 16, 2006
+user_000053	m	29	Germany	Nov 30, 2005
+user_000054	m	28	Brazil	Nov 30, 2004
+user_000055	m	21	Serbia	Aug 6, 2006
+user_000056	m	22	United States	Jan 15, 2006
+user_000057	m	17	United Kingdom	Aug 27, 2006
+user_000058	f	24	Poland	Feb 28, 2006
+user_000059	f	24	United States	Sep 9, 2005
+user_000060	f	22	United States	Oct 11, 2005
+user_000061				Apr 29, 2007
+user_000062		26	Turkey	Mar 11, 2005
+user_000063	f	22	Poland	Sep 25, 2005
+user_000064	m	21	Finland	Dec 19, 2005
+user_000065	m	35	United States	Oct 23, 2005
+user_000066	f	20	United States	May 9, 2006
+user_000067	m	36	Canada	Mar 8, 2005
+user_000068				Oct 16, 2009
+user_000069	m	23	United States	Jul 13, 2005
+user_000070	f		Sweden	Dec 8, 2005
+user_000071	m	7	Netherlands	Dec 13, 2005
+user_000072	f	25	Armenia	Nov 22, 2006
+user_000073	m	34	United Kingdom	May 31, 2007
+user_000074	m	35	Turkey	Jun 9, 2005
+user_000075	f	20	Romania	Jun 4, 2006
+user_000076	m	38	Germany	Nov 26, 2005
+user_000077				Apr 24, 2006
+user_000078	f	27	Poland	Oct 10, 2005
+user_000079	f		United States	Apr 2, 2006
+user_000080				Aug 28, 2006
+user_000081	f	34	United States	Jul 18, 2006
+user_000082	f	20	Poland	Jan 29, 2006
+user_000083	f	19	Bulgaria	May 24, 2007
+user_000084	m	18	Venezuela	Dec 4, 2005
+user_000085	f	18	Croatia	May 14, 2007
+user_000086	f	27		Sep 21, 2007
+user_000087	f	18	Poland	Oct 5, 2006
+user_000088	m	25	Turkey	Mar 13, 2005
+user_000089	f	27	United States	Dec 25, 2005
+user_000090	m	20	Sweden	Aug 20, 2005
+user_000091	f	27	United Kingdom	Mar 18, 2007
+user_000092	m	30	Turkey	Nov 10, 2005
+user_000093	m	20	United Kingdom	Dec 6, 2005
+user_000094	m	22	Poland	Mar 2, 2007
+user_000095	f	4		Jan 27, 2006
+user_000096	f	25	Finland	Feb 20, 2006
+user_000097	m		United Kingdom	Apr 6, 2005
+user_000098	f		New Zealand	Feb 3, 2007
+user_000099	f	22	Brazil	Jun 17, 2006
+user_000100	m	21	United Kingdom	Jun 4, 2007
+user_000101	m	29	United Kingdom	Oct 6, 2006
+user_000102	m		United States	Dec 27, 2006
+user_000103	m	34	United Kingdom	Feb 12, 2006
+user_000104			Norway	Apr 24, 2005
+user_000105	m	23	Turkey	Mar 24, 2006
+user_000106	f	31	New Zealand	Nov 22, 2006
+user_000107	m	15	Poland	Feb 1, 2006
+user_000108	m	23	Latvia	Feb 23, 2006
+user_000109	m	18	Canada	Mar 22, 2006
+user_000110	m	22	Poland	Apr 30, 2007
+user_000111	f	22	Sweden	Feb 17, 2006
+user_000112	f	30	Turkey	Mar 25, 2006
+user_000113	f	22	Italy	Mar 19, 2006
+user_000114	f	28	Turkey	Jan 28, 2007
+user_000115	f		Italy	Jan 14, 2006
+user_000116	f	24	Spain	Dec 9, 2006
+user_000117	m	21	United Kingdom	Aug 27, 2005
+user_000118	f	19	United States	Apr 18, 2005
+user_000119	f	21	Finland	Dec 23, 2005
+user_000120	f	23	United States	Mar 7, 2005
+user_000121	f		United Kingdom	Oct 14, 2005
+user_000122	f	22	United States	Apr 23, 2006
+user_000123	m	26	United States	Feb 5, 2006
+user_000124	m	36	United States	Jul 24, 2005
+user_000125	f	20	Canada	Jun 12, 2005
+user_000126	f			Aug 3, 2006
+user_000127	m	19	Estonia	Mar 3, 2006
+user_000128	m	23	United States	Feb 5, 2006
+user_000129				
+user_000130	f	21	United States	Nov 15, 2006
+user_000131	f	21	United States Minor Outlying Islands	Feb 18, 2006
+user_000132	m	28	Norway	Aug 20, 2005
+user_000133				Jul 6, 2005
+user_000134	m	30	Chile	Oct 17, 2005
+user_000135	m		Czech Republic	Aug 30, 2006
+user_000136		26	United States	Aug 20, 2005
+user_000137	m	19	Chile	Dec 1, 2006
+user_000138	m	22	United States	Dec 19, 2004
+user_000139	m		Antarctica	Oct 4, 2005
+user_000140	m	33	United Kingdom	Mar 25, 2003
+user_000141	f	21	Trinidad and Tobago	Dec 19, 2005
+user_000142			Norway	Oct 14, 2004
+user_000143	m	21	Mexico	Feb 23, 2006
+user_000144	f	19	Norway	Jan 24, 2005
+user_000145	m		United States	May 21, 2007
+user_000146				Apr 4, 2007
+user_000147	m		United States	Dec 29, 2005
+user_000148	f	25	United States	May 2, 2006
+user_000149	m	26	United States	Feb 2, 2006
+user_000150	f	24	Canada	May 1, 2006
+user_000151	m	23	Norway	Mar 25, 2006
+user_000152	m	26	Finland	Oct 18, 2004
+user_000153	m	33		Jan 24, 2005
+user_000154	f		Norway	Mar 17, 2006
+user_000155	m		Venezuela	Jul 11, 2006
+user_000156	m	23	Portugal	Feb 15, 2006
+user_000157	m	19	United States	Dec 27, 2005
+user_000158	f		United States	Mar 23, 2006
+user_000159	m	20	United Kingdom	Jun 20, 2005
+user_000160	m	19	United Kingdom	Jun 23, 2006
+user_000161		26	Netherlands	May 25, 2005
+user_000162	f	48	Switzerland	Jan 3, 2005
+user_000163	m	24	Spain	Nov 4, 2006
+user_000164	f	32	United States	Jun 16, 2006
+user_000165	f	28	Norway	Nov 9, 2005
+user_000166	f	22	Greece	Sep 4, 2006
+user_000167	m	28	Australia	Dec 5, 2005
+user_000168	m	32	United Kingdom	Apr 18, 2004
+user_000169	m	17	Canada	Jun 20, 2006
+user_000170	f	29	Canada	Apr 27, 2005
+user_000171				Oct 29, 2002
+user_000172	m	32	United States	May 19, 2006
+user_000173	f	21	Italy	Mar 6, 2006
+user_000174				
+user_000175	f	28	United Kingdom	Jun 7, 2006
+user_000176	f	24	Nicaragua	Jun 18, 2006
+user_000177	m	42	Brazil	May 25, 2007
+user_000178	m		United Kingdom	Jun 8, 2005
+user_000179	m	28	United Kingdom	Feb 22, 2005
+user_000180	m	20	Ireland	Oct 26, 2005
+user_000181	f	19	United States	Dec 3, 2005
+user_000182	f	22	Belgium	Jul 12, 2005
+user_000183	m			Dec 29, 2006
+user_000184	f	23	Canada	Jun 3, 2006
+user_000185	m	35	United Kingdom	Mar 10, 2005
+user_000186	f		Australia	Aug 9, 2006
+user_000187	m	18	United Kingdom	May 2, 2006
+user_000188	m	23	Finland	May 27, 2005
+user_000189	m	22	United States	Sep 10, 2004
+user_000190	m	26	Italy	Feb 6, 2006
+user_000191	m		United States	Jun 7, 2006
+user_000192	f		Germany	Aug 11, 2007
+user_000193	m	30	Peru	Oct 27, 2005
+user_000194	f		Canada	Oct 8, 2005
+user_000195	m	17	Poland	Jul 14, 2006
+user_000196	m	22	Switzerland	Dec 27, 2004
+user_000197	f	34	Sweden	Apr 18, 2006
+user_000198	f	28	Poland	Jul 17, 2007
+user_000199	m	22	United States	Mar 13, 2006
+user_000200				Oct 20, 2006
+user_000201	m	21	Australia	Oct 4, 2005
+user_000202	m	29	Netherlands	Sep 17, 2006
+user_000203	f		Brazil	Mar 15, 2006
+user_000204	f	22	Turkey	May 20, 2007
+user_000205	m	26	Mexico	Aug 7, 2006
+user_000206	f	27	Turkey	Sep 25, 2005
+user_000207	m	29	Portugal	May 29, 2006
+user_000208	m	21	Switzerland	Jun 17, 2006
+user_000209	f	27	Mexico	Oct 12, 2006
+user_000210	m	42	United Kingdom	Feb 12, 2004
+user_000211	m	24	Canada	Aug 11, 2004
+user_000212	m	21	Romania	Oct 2, 2006
+user_000213	f	55	United Kingdom	May 26, 2005
+user_000214		103	Korea, Democratic People's Republic of	Feb 4, 2007
+user_000215	m	16	Australia	Feb 22, 2006
+user_000216	f			Jan 21, 2007
+user_000217	m	23	United States	Aug 2, 2004
+user_000218	m		Switzerland	Sep 19, 2007
+user_000219	m	34	United States	Nov 8, 2005
+user_000220	f			Jul 27, 2006
+user_000221	m	21	United States	Dec 19, 2006
+user_000222	f	23	Trinidad and Tobago	Aug 10, 2005
+user_000223	m	27	Sweden	Jan 31, 2006
+user_000224	f		Congo, the Democratic Republic of the	Jan 15, 2006
+user_000225	f		Poland	Apr 30, 2007
+user_000226	m	30	Serbia	Nov 11, 2005
+user_000227	m		Belgium	Oct 7, 2006
+user_000228	m	22	Brazil	Oct 26, 2005
+user_000229				Jan 5, 2005
+user_000230	f	18	Australia	May 7, 2006
+user_000231				Aug 9, 2006
+user_000232	m		Poland	Aug 14, 2006
+user_000233	m	25	United States	Jul 20, 2005
+user_000234	f	20	United States	May 27, 2005
+user_000235		29	United States	Nov 28, 2004
+user_000236	f	23	United States	Jul 12, 2005
+user_000237	f	19	United Kingdom	Apr 8, 2006
+user_000238	f	22	Spain	May 8, 2006
+user_000239	m	21	Czech Republic	Dec 2, 2005
+user_000240			United Kingdom	Apr 19, 2004
+user_000241	m	39	United Kingdom	Mar 20, 2007
+user_000242	m	23	Spain	Feb 7, 2006
+user_000243	f	23	Russian Federation	Feb 17, 2007
+user_000244	m	32	United States	Jun 5, 2005
+user_000245	m		United Kingdom	Jan 12, 2006
+user_000246	m	22	United Kingdom	Sep 2, 2007
+user_000247	m	19	Finland	Aug 11, 2006
+user_000248	m	26	United Kingdom	Nov 4, 2003
+user_000249	m	25	United States	Dec 16, 2005
+user_000250	m	21	Canada	Jun 30, 2006
+user_000251	f	24	Poland	May 1, 2006
+user_000252	m	31	Argentina	Aug 25, 2004
+user_000253	f	22	United States	Feb 26, 2006
+user_000254	f	20	Italy	Sep 4, 2006
+user_000255	m	36	United States	Dec 12, 2005
+user_000256	m	23	Germany	Jan 21, 2007
+user_000257	f	18	United States	May 26, 2007
+user_000258	f	75	United States	Jun 21, 2006
+user_000259	f	21	Russian Federation	Jun 24, 2006
+user_000260	m	31	Norway	Apr 15, 2005
+user_000261		20	Norway	Jun 19, 2005
+user_000262	m	19	United States	Dec 10, 2005
+user_000263	f	29	United Kingdom	Aug 14, 2006
+user_000264	m	25	France	Jun 1, 2005
+user_000265	f	21	Poland	Jul 4, 2007
+user_000266	f	28	United Kingdom	Aug 18, 2005
+user_000267	m	30	British Indian Ocean Territory	Oct 11, 2005
+user_000268	m	29	United Kingdom	Mar 28, 2006
+user_000269	f	28	Spain	Sep 17, 2005
+user_000270	f	20	United States	Jan 3, 2006
+user_000271	m	21	Sweden	Dec 11, 2005
+user_000272			United States	May 29, 2007
+user_000273	f	19	United States	Mar 23, 2005
+user_000274	m		Mexico	Feb 12, 2006
+user_000275	f		Spain	Jul 1, 2007
+user_000276	m	25	Hungary	Dec 2, 2005
+user_000277	f	25	New Zealand	Dec 4, 2005
+user_000278	f	20	United States	Jul 30, 2005
+user_000279	f		United States	Nov 5, 2005
+user_000280		32	United Kingdom	Sep 14, 2005
+user_000281	m		Norway	Apr 11, 2005
+user_000282	m	49	Sweden	Mar 16, 2006
+user_000284	m	22	Turkey	Jun 21, 2006
+user_000285	f	23	United States	Dec 7, 2004
+user_000286	m		United States	Oct 18, 2004
+user_000288	m		Netherlands	Mar 22, 2006
+user_000289	f	20	Thailand	Jul 31, 2007
+user_000290	m		United States	Aug 12, 2005
+user_000291	m	30	France	Aug 9, 2006
+user_000292	f	19	Netherlands	Jun 5, 2007
+user_000293		39	United Kingdom	Jan 31, 2005
+user_000294	f	22	Czech Republic	Jan 14, 2006
+user_000295	f	21	Italy	Jun 13, 2007
+user_000296	m	26	Poland	Jan 29, 2006
+user_000297	f	21	Spain	Jun 1, 2007
+user_000298	m	28	Argentina	Jan 26, 2005
+user_000299	f	29	United States	Jul 21, 2006
+user_000300	f	18	United States	Nov 27, 2005
+user_000301	m	20	Netherlands	Jun 3, 2006
+user_000302	m	24		Sep 10, 2005
+user_000303	f	22	Slovakia	Mar 18, 2006
+user_000304	f	22	United Kingdom	Feb 5, 2005
+user_000305	m	21	Sweden	Apr 19, 2006
+user_000306	m	25	Turkey	Jun 6, 2005
+user_000307	m			Oct 16, 2006
+user_000308	f	21	Norway	Mar 3, 2006
+user_000309	m	21	United States	Nov 4, 2005
+user_000310	m	22	United States	Jun 13, 2005
+user_000311	m	23	Canada	Apr 18, 2005
+user_000312	m	40	France	Apr 13, 2006
+user_000313	m	33	United States	Jul 30, 2005
+user_000314	m	21	United States	Dec 19, 2006
+user_000315				Feb 9, 2007
+user_000316	m	20	Belgium	Jun 25, 2006
+user_000317	m	34	Hungary	Jan 19, 2007
+user_000318	m	26	Sweden	Jul 2, 2006
+user_000319	f	21	Brazil	May 22, 2005
+user_000320	m	27	France	Jan 10, 2007
+user_000321	f	18	Poland	Aug 15, 2006
+user_000322	m	38	United Kingdom	Oct 1, 2006
+user_000323	f	24	United States	Aug 21, 2005
+user_000324	m	28	Canada	Jun 7, 2007
+user_000325	f	21	Finland	Oct 26, 2005
+user_000326	m	33	Turkey	Nov 8, 2005
+user_000327	f	19	United Kingdom	May 7, 2006
+user_000328	m	3	United States	Apr 24, 2006
+user_000329	m	21	Belgium	Nov 16, 2006
+user_000330	f	21		Jun 1, 2006
+user_000331	m	35	United Kingdom	May 31, 2005
+user_000332	m	36	United States	Jan 23, 2007
+user_000333				Aug 30, 2006
+user_000334	m	26	United Kingdom	Feb 22, 2005
+user_000335	m		Italy	Mar 21, 2006
+user_000336	f	18	United States	Jun 12, 2006
+user_000337	f	31	United States	Aug 2, 2005
+user_000338	f	28	Poland	Apr 16, 2005
+user_000339	m	32	Norway	Jan 13, 2005
+user_000340	m	22	Brazil	Apr 16, 2007
+user_000341				Jun 4, 2009
+user_000342			Venezuela	Aug 7, 2006
+user_000343	f	26		Jun 1, 2006
+user_000344	m	25	Turkey	Dec 6, 2005
+user_000345	f		United States	May 29, 2005
+user_000346	f	17	Croatia	Apr 30, 2007
+user_000347	m			Nov 24, 2006
+user_000348	m	24	Finland	Jan 4, 2005
+user_000349	m	23	Brazil	Jul 1, 2005
+user_000350	m	21	Poland	Dec 16, 2005
+user_000351	m	24	Norway	Dec 7, 2006
+user_000352		33	Norway	Dec 16, 2004
+user_000353				
+user_000354	m	34	United Kingdom	Dec 22, 2004
+user_000355	m	29	Argentina	Jun 2, 2006
+user_000356	m	27	Turkey	Jun 6, 2005
+user_000357	f		United States	Aug 17, 2005
+user_000358	f		Macedonia	Jul 23, 2006
+user_000359	m	31	United States	Aug 21, 2005
+user_000360		22	Sweden	Mar 10, 2005
+user_000361	f	26	Poland	Jan 16, 2007
+user_000362	m		Germany	Aug 1, 2006
+user_000363			Slovenia	May 21, 2007
+user_000364	f	29	Norway	Mar 16, 2006
+user_000366	m	24	Finland	Dec 12, 2004
+user_000367				Jan 6, 2006
+user_000368	m		United Kingdom	Nov 4, 2003
+user_000369	f		Australia	May 1, 2004
+user_000370	m		Germany	May 6, 2006
+user_000371	m		Algeria	Jul 13, 2005
+user_000372	m		Singapore	Jan 4, 2007
+user_000373	m		Canada	Apr 5, 2006
+user_000374	f		Norway	Mar 8, 2006
+user_000375			United Kingdom	Sep 21, 2005
+user_000376	f		Germany	Aug 13, 2005
+user_000377	m		Portugal	Dec 6, 2005
+user_000378	m		United Kingdom	Oct 5, 2005
+user_000379	m		United Kingdom	Feb 2, 2005
+user_000380			Sweden	Jun 20, 2005
+user_000381	m		Turkey	Sep 24, 2005
+user_000382	m		United States	Mar 19, 2005
+user_000383			Zimbabwe	Aug 31, 2006
+user_000384	m		United Kingdom	Sep 28, 2004
+user_000385	m		Latvia	Apr 28, 2006
+user_000386	f		Poland	Jun 7, 2006
+user_000387	m		United Kingdom	Jul 21, 2005
+user_000388	m		United States	Feb 19, 2006
+user_000389	f		United States	Oct 20, 2005
+user_000390				Oct 1, 2005
+user_000391	f		Germany	Oct 22, 2004
+user_000392	m		Spain	Jun 8, 2006
+user_000393	m		United States	Feb 7, 2007
+user_000394	m		United States	Jun 21, 2006
+user_000395	m		Spain	May 16, 2007
+user_000396	f		Japan	May 10, 2006
+user_000397	f		United States	Feb 16, 2006
+user_000398	m		France	Nov 21, 2006
+user_000399	m		United Kingdom	Sep 12, 2005
+user_000400	m		United States	Feb 26, 2006
+user_000401	m		France	May 10, 2006
+user_000402	f		Serbia	Jun 5, 2006
+user_000403	f		United States	May 17, 2006
+user_000404	m		United States	Apr 9, 2006
+user_000405	f		Australia	Sep 22, 2005
+user_000406	m		France	Feb 2, 2006
+user_000407	m		United Kingdom	Apr 26, 2004
+user_000408	m		United States	Jul 19, 2007
+user_000409	m		Portugal	Mar 8, 2006
+user_000410	m		United States	Feb 22, 2006
+user_000411	m		Venezuela	Aug 8, 2006
+user_000412	f		United Kingdom	Sep 30, 2005
+user_000413	f		United States	Sep 12, 2006
+user_000414	m		United Kingdom	Apr 5, 2004
+user_000415	m		United Kingdom	Aug 5, 2006
+user_000416				Feb 12, 2005
+user_000417	m		United Kingdom	Apr 30, 2006
+user_000418	m		China	Jan 13, 2007
+user_000419	f		United Kingdom	Nov 23, 2006
+user_000420				Aug 17, 2004
+user_000421	m		United States	Apr 22, 2006
+user_000422	f		Finland	Mar 23, 2006
+user_000423			Netherlands	Apr 1, 2006
+user_000424	m		Netherlands	Oct 8, 2006
+user_000425	m		Finland	Jan 12, 2005
+user_000426	m		Australia	Jul 8, 2006
+user_000427	m		United States	Jan 4, 2004
+user_000429			Turkey	Jan 15, 2006
+user_000430				May 18, 2006
+user_000431	m		Poland	Aug 28, 2006
+user_000432	m		Spain	Nov 26, 2006
+user_000433	f		United States	Jan 24, 2006
+user_000434	f		United States	Jun 16, 2006
+user_000435	f		Poland	Oct 18, 2007
+user_000436	f		Poland	Jan 19, 2006
+user_000437	f		Russian Federation	Jan 11, 2006
+user_000438	f		United States	Sep 17, 2006
+user_000439	f		United Kingdom	Sep 8, 2006
+user_000440	f		Malta	Feb 4, 2006
+user_000441	m		United States	Mar 16, 2005
+user_000442	f		France	Dec 24, 2005
+user_000443	f		Canada	Feb 8, 2006
+user_000444	f		Germany	Dec 12, 2005
+user_000445			Finland	Dec 5, 2006
+user_000446	f		United States	Apr 21, 2005
+user_000447				Sep 5, 2007
+user_000448	f		United Kingdom	Feb 4, 2006
+user_000449	m		United States	Feb 11, 2007
+user_000450	m		United States	Dec 7, 2004
+user_000451	m		United Kingdom	Aug 30, 2004
+user_000452				Nov 11, 2005
+user_000453	m		Finland	Oct 17, 2005
+user_000454	f		Poland	Jul 18, 2007
+user_000455	m			Sep 1, 2003
+user_000456	m		Russian Federation	Jul 27, 2006
+user_000457	m		Brazil	Jun 13, 2006
+user_000458	f		Argentina	Feb 1, 2006
+user_000459			Hungary	Aug 14, 2004
+user_000460	f		Canada	Sep 27, 2005
+user_000461	m		Germany	Dec 16, 2005
+user_000462	f		United States	Feb 7, 2007
+user_000463	m		United States	Mar 19, 2006
+user_000464	f		United States	Apr 19, 2006
+user_000465	f		Russian Federation	Oct 24, 2006
+user_000467	f		Germany	Apr 29, 2006
+user_000468	m		Brazil	Sep 25, 2006
+user_000469	f		Italy	Sep 6, 2006
+user_000471			Poland	Nov 17, 2006
+user_000472	f		United Kingdom	Mar 10, 2005
+user_000473	f		Italy	Apr 19, 2006
+user_000474	f			May 23, 2007
+user_000475	f		United States	Nov 15, 2004
+user_000476	f		Brazil	Jun 2, 2006
+user_000477	m		United States	Feb 9, 2005
+user_000478	m		United States	Jan 4, 2006
+user_000479	m		United States	Feb 11, 2005
+user_000480	f		United States	Jul 20, 2005
+user_000481	m		United States	Aug 16, 2005
+user_000482	f		Russian Federation	Mar 12, 2006
+user_000483			United States	May 23, 2005
+user_000484	m		Italy	Dec 20, 2006
+user_000485	m		United Kingdom	Aug 7, 2006
+user_000486	m		United Kingdom	Sep 6, 2005
+user_000487	m		Netherlands	Mar 8, 2006
+user_000488	m		Peru	Dec 19, 2006
+user_000489	m		Germany	Mar 25, 2006
+user_000490	m		Italy	May 23, 2006
+user_000491	f		United States	Jan 28, 2006
+user_000492	f		Thailand	Jul 4, 2007
+user_000493	m		United States	Dec 10, 2005
+user_000494	m		United Kingdom	Jun 16, 2006
+user_000495	m		Australia	Dec 4, 2004
+user_000496	m		Romania	Jul 26, 2005
+user_000497	f		United States	Feb 16, 2008
+user_000498	f		Poland	Jun 23, 2007
+user_000499	f			Jul 5, 2006
+user_000500			United States	Sep 23, 2007
+user_000501	f		United States	Mar 13, 2006
+user_000502	f		United States	Jan 22, 2006
+user_000503	f		Austria	Dec 3, 2005
+user_000504			United States	Dec 3, 2004
+user_000505	f			Nov 12, 2006
+user_000506	f			Jan 9, 2006
+user_000507	m		Italy	Aug 17, 2006
+user_000508	m		United Kingdom	Oct 26, 2006
+user_000509			United Kingdom	Sep 18, 2005
+user_000510	m		Germany	Mar 5, 2006
+user_000511	f		United States	Dec 14, 2005
+user_000512	f		United Kingdom	Feb 2, 2005
+user_000513			United Kingdom	Jan 5, 2005
+user_000514	m		United Kingdom	Jul 12, 2005
+user_000515	m		Netherlands	Mar 30, 2006
+user_000516	m		Austria	Jul 25, 2007
+user_000517	f		Italy	Feb 24, 2006
+user_000518	f		United Kingdom	Feb 13, 2006
+user_000519	m		France	Nov 2, 2006
+user_000520	m		Venezuela	Aug 24, 2005
+user_000521	f		Germany	Jun 28, 2007
+user_000522	f		Norway	Nov 9, 2005
+user_000523	f		Mexico	Apr 29, 2007
+user_000524	f		United Kingdom	Jul 19, 2007
+user_000525				Dec 14, 2004
+user_000526	f		United States	Dec 3, 2005
+user_000527	f		Chile	Nov 1, 2006
+user_000528	f			Apr 14, 2007
+user_000529	m		United States	May 5, 2006
+user_000530	m		Russian Federation	Aug 29, 2006
+user_000531	f		Bulgaria	Jul 6, 2006
+user_000532	m		Netherlands	Dec 1, 2006
+user_000533	m	27	Japan	Nov 5, 2009
+user_000534	f		Sweden	Sep 7, 2004
+user_000535	m		Turkey	Dec 16, 2004
+user_000536	m		Spain	Jun 7, 2005
+user_000537			Poland	Aug 8, 2007
+user_000538	f		Portugal	Apr 17, 2007
+user_000539	m		Venezuela	Aug 23, 2006
+user_000540	f		Spain	May 24, 2007
+user_000541	f		United Kingdom	Nov 15, 2006
+user_000542	f		Mexico	Jan 18, 2006
+user_000543	m		United Kingdom	Oct 28, 2004
+user_000544	f		United States	Jul 17, 2006
+user_000545	m		Argentina	Jun 13, 2006
+user_000546	f		Germany	Jul 24, 2006
+user_000547	m		United States	Jul 20, 2005
+user_000548	m		United States	Mar 30, 2007
+user_000549	m		Australia	Jan 28, 2006
+user_000550	m		United Kingdom	Jun 16, 2005
+user_000551	m		Australia	Apr 27, 2007
+user_000552	m		Chile	Dec 3, 2006
+user_000553	m		Italy	Aug 19, 2005
+user_000554	m		United Kingdom	Mar 16, 2006
+user_000555	f		Spain	Oct 14, 2005
+user_000556			Netherlands	May 12, 2006
+user_000557	f		United States	Mar 6, 2005
+user_000558	f		United States	May 20, 2003
+user_000559	f		United States	Aug 24, 2006
+user_000560	f		United States	Nov 30, 2006
+user_000561	f			Mar 18, 2006
+user_000562	m		United Kingdom	Dec 16, 2005
+user_000563	m		Italy	Dec 27, 2006
+user_000564	f		United States	Jan 17, 2006
+user_000565	f		Germany	Feb 18, 2006
+user_000566	f		Netherlands Antilles	Sep 26, 2006
+user_000567	f		Germany	Feb 9, 2007
+user_000568	m		Canada	Mar 3, 2006
+user_000569	f		United States	May 31, 2005
+user_000570	m		Brazil	Jun 27, 2005
+user_000571	m		United States	Jul 18, 2005
+user_000572	m		Israel	Oct 9, 2004
+user_000573	f		Australia	Jun 20, 2006
+user_000574	m		Poland	Feb 25, 2006
+user_000575	m		United States	Jul 30, 2004
+user_000576	m		United States	Nov 2, 2005
+user_000577	m		Estonia	Mar 11, 2006
+user_000578	m		Germany	Aug 27, 2003
+user_000579	f		United Kingdom	Sep 24, 2007
+user_000580	f		United Kingdom	Jul 26, 2005
+user_000581	m		Turkey	Dec 20, 2005
+user_000582	f		France	Mar 5, 2006
+user_000583	f		Finland	Feb 28, 2006
+user_000584	f		Canada	Apr 28, 2006
+user_000585			United Kingdom	Apr 12, 2004
+user_000586	f		United States	May 30, 2006
+user_000587	m		United Kingdom	Jul 6, 2004
+user_000588				
+user_000589	f		Russian Federation	Mar 13, 2006
+user_000590	f		Norway	Mar 9, 2006
+user_000591	f		Canada	Sep 12, 2005
+user_000592	m		United States	May 24, 2007
+user_000593	f			Jun 29, 2006
+user_000594	m		Netherlands	May 16, 2006
+user_000595	m		Russian Federation	Mar 27, 2007
+user_000596	m		United States	Feb 13, 2006
+user_000597	m			Apr 20, 2006
+user_000598	f		United States	Mar 1, 2006
+user_000599			United Kingdom	Apr 21, 2003
+user_000600	f		United States	Apr 28, 2006
+user_000601	m		Poland	Jun 7, 2007
+user_000602	m		Canada	Dec 4, 2005
+user_000603	m		United Kingdom	Feb 25, 2006
+user_000604	m		Canada	Aug 7, 2006
+user_000605	m		United States	Oct 10, 2005
+user_000606	f		Bulgaria	Jun 15, 2006
+user_000607	m		Sweden	Apr 29, 2006
+user_000608	f		Poland	May 21, 2005
+user_000609	m		New Zealand	Nov 5, 2006
+user_000610	m		Bulgaria	Jan 22, 2006
+user_000611	m		Italy	Jan 9, 2007
+user_000612	m		Finland	Mar 25, 2005
+user_000613	f		Poland	Jan 21, 2007
+user_000614	m		Germany	Dec 16, 2006
+user_000615	f		Sweden	Jan 10, 2005
+user_000616	f		Slovenia	May 31, 2006
+user_000617	m		United Kingdom	Mar 15, 2006
+user_000618	f		United States	Dec 19, 2006
+user_000619			United Kingdom	May 1, 2004
+user_000620	f		Finland	Jan 7, 2005
+user_000621	f			Aug 24, 2006
+user_000622				Jun 3, 2006
+user_000623	m		Chile	Dec 9, 2006
+user_000624	f		Norway	Mar 14, 2006
+user_000625	m		Argentina	Mar 4, 2006
+user_000626	f		Spain	Apr 16, 2006
+user_000627	f		Russian Federation	Aug 13, 2007
+user_000628	m		Slovakia	Apr 23, 2005
+user_000629	m		Poland	Jan 26, 2007
+user_000630	f		United States	May 22, 2005
+user_000631	m		Finland	Oct 14, 2005
+user_000632	m		United Kingdom	Feb 1, 2006
+user_000633	f			Jan 17, 2006
+user_000634	f		United States	Aug 14, 2005
+user_000635	m		Serbia	Apr 11, 2005
+user_000636	f		Bulgaria	Jun 20, 2006
+user_000637	f		Poland	Feb 5, 2007
+user_000638				Dec 19, 2006
+user_000639	m		Mexico	Apr 27, 2005
+user_000640	f		United States	Apr 6, 2006
+user_000641	f		Sweden	Jan 13, 2006
+user_000642	f		Canada	Apr 1, 2006
+user_000643	m		France	Jul 15, 2006
+user_000644	f		United States	Sep 11, 2005
+user_000645	m		Hungary	Nov 12, 2006
+user_000646	m		Peru	Dec 15, 2006
+user_000647	m		United States	Mar 24, 2006
+user_000648	f		Netherlands	Oct 18, 2005
+user_000649	m		United States	Feb 8, 2007
+user_000650	m		United States	Dec 16, 2007
+user_000651	m		United States	Dec 17, 2007
+user_000652	m		Germany	Aug 18, 2007
+user_000653	m		Brazil	May 14, 2006
+user_000654	m		Canada	Feb 22, 2006
+user_000655	m		Portugal	Oct 9, 2006
+user_000656	f		United States	Jan 29, 2006
+user_000657	m		Sweden	Sep 30, 2005
+user_000658	f		Latvia	Apr 11, 2007
+user_000659			Belgium	Jan 27, 2005
+user_000660	m		Croatia	Aug 12, 2007
+user_000661	m		Netherlands	Sep 25, 2006
+user_000662	f		United States	Jun 13, 2006
+user_000664	m		Germany	Aug 15, 2005
+user_000665	f		Czech Republic	Feb 28, 2006
+user_000666	m		Poland	May 24, 2005
+user_000667	f		United Kingdom	Mar 3, 2006
+user_000668	f		United States	Nov 26, 2005
+user_000669	m		Colombia	May 7, 2007
+user_000670	f		Argentina	Feb 19, 2006
+user_000671	m		Czech Republic	Jan 23, 2006
+user_000672	m		United States	Apr 10, 2006
+user_000673	m		Mexico	May 25, 2007
+user_000674	f		Poland	Jun 6, 2007
+user_000675	m		Poland	Jul 20, 2005
+user_000676	m		Netherlands	Nov 15, 2006
+user_000677	f		Netherlands	May 31, 2006
+user_000678	m		United Kingdom	Dec 9, 2003
+user_000679	f		United States	Apr 8, 2004
+user_000680				May 23, 2005
+user_000681	m		United States	Dec 20, 2004
+user_000682	f		Norway	Aug 20, 2006
+user_000683	f		United Kingdom	Apr 18, 2006
+user_000684	m		Canada	Jun 11, 2005
+user_000685	m		Norway	Oct 13, 2004
+user_000686	f		United States	Feb 23, 2007
+user_000687	f		United Kingdom	Mar 11, 2005
+user_000688	f		United Kingdom	Apr 17, 2007
+user_000689	m		United Kingdom	Oct 13, 2005
+user_000690	m		United Kingdom	Jun 25, 2007
+user_000691	m		United States	Jun 30, 2006
+user_000692	f		Romania	Jul 6, 2006
+user_000693	m		United States	Nov 21, 2005
+user_000694	m		United Kingdom	Jun 7, 2005
+user_000695	m		Australia	Aug 2, 2005
+user_000696	f		Romania	Aug 30, 2006
+user_000697	f		United States	May 8, 2006
+user_000698				Aug 13, 2005
+user_000699			Russian Federation	Mar 3, 2007
+user_000700	m		Belarus	Feb 4, 2007
+user_000701	m		United Kingdom	Apr 2, 2004
+user_000702	m		Poland	Dec 2, 2006
+user_000703	f		United States	Mar 13, 2006
+user_000704	f		Australia	Apr 27, 2007
+user_000705	f		India	Jun 3, 2006
+user_000706	f		Romania	May 11, 2006
+user_000707			Norway	Apr 12, 2005
+user_000708				May 23, 2009
+user_000709	m		Canada	Oct 2, 2004
+user_000710	m		Estonia	Apr 24, 2007
+user_000711	m		United States	Jan 21, 2006
+user_000712	m		Poland	Mar 4, 2004
+user_000713	f		Norway	Apr 21, 2005
+user_000714	m		Colombia	Sep 23, 2006
+user_000715	m		Australia	May 13, 2007
+user_000716	f		Portugal	Mar 22, 2007
+user_000717				Jul 13, 2007
+user_000718	f			Feb 14, 2006
+user_000719	m		Brazil	Oct 30, 2005
+user_000720	f		Norway	Jun 29, 2007
+user_000721	m		Poland	Apr 7, 2005
+user_000722	m		Turkey	Apr 17, 2005
+user_000723	f		United States	Jun 4, 2006
+user_000724	m		United States	Feb 21, 2005
+user_000725	f		Turkey	Aug 21, 2007
+user_000726	m		Austria	Apr 7, 2006
+user_000727	f		United States	Jul 21, 2005
+user_000728	f		Finland	Mar 12, 2006
+user_000729	m		Ireland	Oct 26, 2005
+user_000730	m		Canada	May 10, 2006
+user_000731	m		Tunisia	Nov 6, 2005
+user_000732	f		Finland	Jan 3, 2005
+user_000733	m		United Kingdom	Sep 29, 2003
+user_000734				
+user_000735			Norway	Dec 11, 2004
+user_000736	m		Lithuania	Apr 8, 2007
+user_000737	m		Germany	Jul 7, 2007
+user_000738	m		Belgium	Aug 20, 2006
+user_000739	m		Australia	May 25, 2006
+user_000740	m		Italy	Jan 10, 2007
+user_000741	m		United States	May 11, 2006
+user_000742	m		Germany	Jun 10, 2007
+user_000743	m		United Kingdom	Jul 17, 2005
+user_000744	m		United States	Jan 21, 2004
+user_000745	m			Jan 28, 2005
+user_000746	m		Netherlands	Oct 29, 2006
+user_000747	f		Poland	Apr 21, 2006
+user_000748	m		Brazil	Apr 18, 2006
+user_000749	f		Portugal	Jul 14, 2006
+user_000750	m		Italy	Aug 27, 2006
+user_000751	m		United States	Jun 14, 2005
+user_000752	m		United States	Sep 17, 2004
+user_000753				Dec 3, 2004
+user_000754	m		United States	Jul 10, 2006
+user_000755	m		United Kingdom	Dec 12, 2004
+user_000756	m		United States	Jul 12, 2006
+user_000757	f		United States	Mar 30, 2005
+user_000758	f		United States	Feb 19, 2006
+user_000759	f		Australia	Feb 26, 2006
+user_000760			Lithuania	Mar 12, 2006
+user_000761			Netherlands	Nov 30, 2006
+user_000762	f		United Kingdom	Jul 4, 2005
+user_000763				Sep 25, 2007
+user_000764	m		United Kingdom	Feb 20, 2005
+user_000765	f		Germany	Oct 26, 2005
+user_000766	f		Norway	Sep 6, 2006
+user_000767	m		United States	Jan 22, 2006
+user_000768	m		Romania	Jun 4, 2007
+user_000769	m		Japan	Nov 30, 2005
+user_000770	f		United States	Oct 18, 2006
+user_000771	f		Chile	Sep 4, 2006
+user_000772	f		Canada	May 17, 2007
+user_000773	m		United States	Dec 9, 2004
+user_000774	m		United States	Aug 25, 2005
+user_000775	m		Finland	Oct 9, 2007
+user_000776	f		Italy	Jan 10, 2007
+user_000777	m		Sweden	May 24, 2005
+user_000778			United States	Jun 5, 2005
+user_000779	f			Jul 28, 2004
+user_000780	f		Lithuania	Jan 2, 2007
+user_000781	f		United States	Dec 4, 2005
+user_000782	f		United States	Sep 18, 2004
+user_000783	m		United States	Feb 13, 2006
+user_000784	f		Netherlands	Aug 14, 2005
+user_000785				Oct 2, 2007
+user_000786	m		Russian Federation	Sep 17, 2007
+user_000787	f		United Kingdom	Feb 23, 2006
+user_000788	m		Germany	Apr 12, 2005
+user_000789	f		United States	Mar 28, 2005
+user_000790	f			Feb 1, 2006
+user_000791	m		United Kingdom	Sep 22, 2004
+user_000792	m			Jan 30, 2005
+user_000793	m		China	Apr 26, 2004
+user_000794	f		Iceland	Mar 26, 2006
+user_000795	f		United States	Jul 12, 2006
+user_000796	f		Croatia	Mar 5, 2006
+user_000797	f		Lithuania	Nov 4, 2006
+user_000798	f		Finland	Jun 28, 2005
+user_000799	m		Germany	Sep 26, 2004
+user_000800			Russian Federation	Jul 17, 2005
+user_000801				Apr 17, 2007
+user_000802	f		United Kingdom	Jun 12, 2006
+user_000803	m		Norway	Dec 9, 2005
+user_000804	m		Norway	Dec 9, 2005
+user_000805	m		Belgium	Aug 1, 2006
+user_000806				
+user_000807	m		United States	Jul 30, 2004
+user_000808	m		Russian Federation	Dec 16, 2006
+user_000809	m		Finland	Jun 8, 2005
+user_000810	m		United Kingdom	Mar 5, 2006
+user_000811	f		United States	Dec 6, 2005
+user_000812			Belgium	Nov 12, 2006
+user_000813	m		New Zealand	Jun 16, 2007
+user_000814	m		Germany	May 20, 2006
+user_000815	f		United Kingdom	Jul 10, 2006
+user_000816	m		United Kingdom	Dec 13, 2005
+user_000817	m		Mexico	Nov 12, 2006
+user_000818	m		Finland	Jul 9, 2005
+user_000819	m		United Kingdom	Feb 22, 2005
+user_000820	m		Thailand	Feb 4, 2005
+user_000821	f		France	Nov 26, 2005
+user_000822				May 25, 2007
+user_000823			Norway	Mar 22, 2007
+user_000824	m		United Kingdom	Mar 5, 2006
+user_000825	m		United Kingdom	Dec 15, 2005
+user_000826	f		United States	Aug 7, 2007
+user_000827	m		Switzerland	Nov 6, 2005
+user_000828			Japan	May 24, 2005
+user_000829	m		Peru	Feb 27, 2006
+user_000830	m		Ireland	Dec 6, 2005
+user_000831	m		United Kingdom	Dec 7, 2005
+user_000832	f		United Kingdom	Aug 10, 2005
+user_000833	f		United States	Feb 20, 2005
+user_000834	f		Italy	Apr 7, 2006
+user_000835	f		Australia	Jul 1, 2006
+user_000836	m		Norway	Feb 8, 2006
+user_000837	f		United States	May 31, 2005
+user_000838	f		United Kingdom	May 17, 2006
+user_000839	m		Greece	Feb 7, 2006
+user_000840	f		Russian Federation	Jan 24, 2006
+user_000841	m		Spain	Nov 5, 2005
+user_000842	m		United States	Jul 18, 2005
+user_000843	m		United States	Feb 25, 2007
+user_000844			Norway	Apr 10, 2004
+user_000845	f		Finland	Jun 5, 2006
+user_000846	m		United Kingdom	Jan 4, 2007
+user_000847	f		Germany	Dec 30, 2005
+user_000848	f		Poland	Sep 12, 2005
+user_000849	m		United Kingdom	Aug 20, 2006
+user_000850				Jul 25, 2006
+user_000851				Jan 6, 2005
+user_000852	f		Trinidad and Tobago	Dec 22, 2005
+user_000853	f		Morocco	Apr 21, 2007
+user_000854	f		Norway	May 19, 2007
+user_000855				Nov 3, 2006
+user_000856	f			May 20, 2004
+user_000857	f		United States	Jul 27, 2006
+user_000858	m		United States	Nov 25, 2004
+user_000859	m		Italy	Jan 9, 2006
+user_000860	m		Turkey	Apr 1, 2006
+user_000861	m		United States	Sep 10, 2005
+user_000862	f		Italy	Feb 10, 2006
+user_000863			United Kingdom	Oct 15, 2004
+user_000864	m		Norway	Dec 10, 2005
+user_000865	f		Croatia	May 31, 2007
+user_000866	f		United States	Oct 7, 2005
+user_000867	f		United States	Feb 7, 2006
+user_000868	m		United States	Jun 15, 2006
+user_000869	m		France	May 23, 2007
+user_000870	m		United Kingdom	Mar 10, 2003
+user_000871			Argentina	Feb 3, 2005
+user_000872	f		Brazil	Jan 31, 2008
+user_000873	m		United Kingdom	Mar 5, 2004
+user_000874	m			Apr 3, 2006
+user_000875	f		Slovakia	Mar 9, 2006
+user_000876	f		Brazil	Dec 3, 2006
+user_000877	m			Jan 9, 2005
+user_000878	f		United States	Dec 1, 2005
+user_000879	f		United States	Dec 31, 2006
+user_000880				Nov 5, 2006
+user_000881	m		United States	Mar 18, 2006
+user_000882	m		United States	Apr 29, 2004
+user_000883	m		Spain	Oct 6, 2005
+user_000884	m		Poland	May 2, 2007
+user_000885	m		United States	Jun 8, 2005
+user_000886	m		Finland	Nov 3, 2006
+user_000887	f		United States	Feb 14, 2005
+user_000888	m		Norway	Feb 28, 2005
+user_000889	m		Sweden	Apr 26, 2006
+user_000890	m		United States	Dec 6, 2005
+user_000891	f		Italy	Feb 9, 2007
+user_000892			United States	Sep 1, 2006
+user_000893	m		United States	Mar 29, 2006
+user_000894	m		Colombia	Dec 2, 2005
+user_000895	m		Canada	Feb 2, 2006
+user_000896	m		United States	Apr 2, 2003
+user_000897				Feb 18, 2007
+user_000898	m		United States	Nov 28, 2003
+user_000899	m		Canada	Oct 7, 2005
+user_000900	f		Poland	Mar 17, 2006
+user_000901	f		Argentina	May 9, 2006
+user_000902	f			Feb 3, 2006
+user_000903	m		Belgium	Dec 18, 2006
+user_000904	m		United States	Dec 15, 2006
+user_000905	m		Brazil	Apr 18, 2006
+user_000906	m		Germany	Jan 12, 2006
+user_000907	f		Poland	Jan 26, 2007
+user_000908	m		Germany	Apr 5, 2005
+user_000909	m		Sweden	Apr 29, 2005
+user_000910	m		United States	Sep 26, 2004
+user_000911	m		United States	Jul 9, 2005
+user_000912	m		United Kingdom	Jan 8, 2006
+user_000913	m		United States	Aug 26, 2004
+user_000914			United States	Jan 29, 2005
+user_000915			Finland	Dec 15, 2004
+user_000916	f		Trinidad and Tobago	Mar 7, 2006
+user_000917	m		United States	Mar 14, 2006
+user_000918	m			May 15, 2006
+user_000919	m		United States	May 31, 2007
+user_000920	f		United States	Sep 14, 2005
+user_000921	m		Russian Federation	May 23, 2006
+user_000922	m		United Kingdom	May 21, 2004
+user_000923	m		United Kingdom	Jan 28, 2005
+user_000924	f		United States	May 30, 2005
+user_000925	f		Norway	Mar 6, 2006
+user_000926	f		Turkey	Sep 27, 2006
+user_000927	m		Poland	Aug 29, 2006
+user_000928	m		United States	Aug 11, 2005
+user_000929	m		Turkey	Nov 21, 2004
+user_000930	f			Jun 14, 2007
+user_000931	m		Italy	Feb 19, 2007
+user_000932	f		Poland	Jun 2, 2007
+user_000933	m		Turkey	May 3, 2006
+user_000934	m		Northern Mariana Islands	May 13, 2006
+user_000935	f		Finland	Oct 24, 2004
+user_000936	f		Finland	Jul 3, 2005
+user_000937	f		United Kingdom	Jun 30, 2007
+user_000938	m		United States	Sep 29, 2004
+user_000939	f		Canada	Dec 19, 2005
+user_000940	m		United Kingdom	Jan 4, 2007
+user_000941	m		Spain	Dec 16, 2006
+user_000942	m		Germany	Sep 5, 2004
+user_000943	m		Sweden	Oct 9, 2005
+user_000944	m		Ireland	Jul 18, 2006
+user_000945	f		United Kingdom	May 27, 2006
+user_000946			Netherlands	Apr 11, 2006
+user_000947	m		Australia	Sep 18, 2006
+user_000948	m		United States	Apr 23, 2006
+user_000949	f		United States	May 30, 2005
+user_000950			Chile	Jan 13, 2007
+user_000951	f		Netherlands	Feb 28, 2006
+user_000952	f		Estonia	May 31, 2006
+user_000953	m		United Kingdom	Aug 15, 2007
+user_000954	m		Germany	Dec 4, 2005
+user_000955	f		France	Jun 8, 2006
+user_000956	f		Poland	Sep 25, 2007
+user_000957	m		Finland	Mar 29, 2005
+user_000958	m		Mexico	Aug 26, 2005
+user_000959	m		United States	Jun 3, 2006
+user_000960	m		United States	Jul 9, 2007
+user_000961	m		United States	Apr 7, 2003
+user_000962	m		United States	Apr 3, 2004
+user_000963				Apr 19, 2004
+user_000964	m		China	Oct 27, 2006
+user_000965	m		Netherlands	Jul 20, 2007
+user_000966			United States	May 20, 2004
+user_000967	f		Bosnia and Herzegovina	Jan 12, 2006
+user_000968	m		Germany	Nov 8, 2005
+user_000969	m		Russian Federation	Dec 18, 2006
+user_000970	m		New Zealand	Feb 6, 2006
+user_000971	f		United States	Jan 12, 2005
+user_000972	f		Russian Federation	Nov 13, 2006
+user_000973	m		Russian Federation	Feb 23, 2007
+user_000974	f		United States	Apr 29, 2006
+user_000975				Mar 23, 2006
+user_000976				
+user_000977	m		United Kingdom	Mar 7, 2006
+user_000978	m		Turkey	Aug 24, 2005
+user_000979	f		Turkey	Apr 25, 2007
+user_000980	f		United Kingdom	Jun 16, 2007
+user_000981	f		Croatia	Mar 29, 2006
+user_000982	f		Turkey	Oct 28, 2006
+user_000983	f		Canada	Nov 5, 2004
+user_000984	f		United States	Jul 2, 2005
+user_000985	f		Australia	May 22, 2006
+user_000986	m		Sweden	Apr 12, 2005
+user_000987	f		United States	Oct 30, 2006
+user_000989	f		Canada	Nov 30, 2005
+user_000990	f		Bulgaria	Sep 15, 2005
+user_000991	f		Finland	Jan 29, 2006
+user_000992	m		Sweden	Nov 22, 2006
+user_000993	m		Finland	Sep 6, 2006
+user_000994	m			Apr 18, 2006
+user_000995	f		Australia	Jan 22, 2006
+user_000996	f		United States	Jul 17, 2006
+user_000997	m		United States	Jan 5, 2007
+user_000998	m		United Kingdom	Sep 28, 2005
+user_000999	f		Poland	Jul 24, 2007
+user_001000	m		United States	Mar 24, 2007

lastfm_360k/README.txt

@@ -0,0 +1,64 @@
+===================
+lastfm-dataset-360K
+===================
+
+Version 1.2
+March 2010
+
+. What is this?
+
+    This dataset contains <user, artist, plays> tuples collected from Last.fm API ( http://www.last.fm/api ), 
+    using the user.getTopArtists() method ( http://www.last.fm/api/show?service=300 )
+
+. Data Format:
+
+    The data is formatted one entry per line as follows (tab separated):
+
+    usersha1-artmbid-artname-plays.tsv:
+      user-mboxsha1 \t musicbrainz-artist-id \t artist-name \t plays
+
+    usersha1-profile.tsv:
+      user-mboxsha1 \t gender ('m'|'f'|empty) \t age (int|empty) \t country (str|empty) \t signup (date|empty)
+
+. Example:
+
+    usersha1-artmbid-artname-plays.tsv:
+      000063d3fe1cf2ba248b9e3c3f0334845a27a6bf    af8e4cc5-ef54-458d-a194-7b210acf638f    cannibal corpse    48
+      000063d3fe1cf2ba248b9e3c3f0334845a27a6bf    eaaee2c2-0851-43a2-84c8-0198135bc3a8    elis    31
+      ...
+
+    usersha1-profile.tsv
+      000063d3fe1cf2ba248b9e3c3f0334845a27a6bf    m    19    Mexico    Apr 28, 2008
+      ...
+
+. Data Statistics:
+
+     Total Lines:           17,559,530
+     Unique Users:             359,347
+     Artists with MBID:        186,642
+     Artists without MBDID:    107,373
+
+. Files:
+
+    usersha1-artmbid-artname-plays.tsv (MD5: be672526eb7c69495c27ad27803148f1)
+    usersha1-profile.tsv               (MD5: 51159d4edf6a92cb96f87768aa2be678)
+    mbox_sha1sum.py                    (MD5: feb3485eace85f3ba62e324839e6ab39)
+
+. License:
+
+    The data in lastfm-dataset-360K is distributed with permission of Last.fm. 
+    The data is made available for non-commercial use.
+    Those interested in using the data or web services in a commercial context 
+    should contact: partners [at] last [dot] fm. 
+    For more information see http://www.last.fm/api/tos
+
+. Acknowledgements:
+
+    Thanks to Last.fm for providing the access to the <user,artist,plays> data via their
+    web services. 
+    Special thanks to Norman Casagrande.
+
+. Contact:
+
+    This data was collected by Oscar Celma. Send questions or comments to oscar.celma@upf.edu
+

lastfm_360k/usersha1-profile.tsv

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b0e03e8ddd759063c53d8468925f74779361e578d2630e221529101125936fc3
+size 24644914

mind/metadata.json

@@ -0,0 +1,9 @@
+{
+  "text_embedding_dim": 384,
+  "pca_dim": 128,
+  "entity_embedding_dim": 100,
+  "max_history_len": 50,
+  "num_categories": 18,
+  "num_subcategories": 285,
+  "pca_explained_variance": 0.7815868854522705
+}

movielens_10m/ml-10M100K/README.html

@@ -0,0 +1,334 @@
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Strict//EN"
+  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-strict.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml" lang="en" xml:lang="en">
+  <head>
+    <meta http-equiv="Content-Type" content="text/html;charset=utf-8" />
+    <style type="text/css">
+      h1 {
+        color:#fc3;
+        font-family:"Lucida Grande",Verdana,sans-serif; 
+        font-size: 150%; 
+        font-weight: normal; 
+        margin:34px 0 0;
+        background-color: #7A0019;
+      }
+      p {
+        margin-left: 20px;
+      }
+      p.file_line_structure {
+        margin-left: 40px;
+      }
+      table {
+        margin-left: 30px;
+      }
+      th {
+        text-align:left;
+      }
+    </style>
+
+    <title>MovieLens 10M/100k Data Set README</title>
+  </head>
+  <body>
+    <h1>
+        Summary
+    </h1>
+    <p>
+      This data set contains 10000054 ratings and 95580 tags 
+      applied to 10681 movies by 71567 users of the 
+      online movie recommender service <a href="http://www.movielens.org">MovieLens</a>.
+    </p>
+    <p>
+      Users were selected at random for inclusion.  All users selected had rated 
+      at least 20 movies.  Unlike previous MovieLens data sets, no demographic 
+      information is included.  Each user is represented by an id, and no other 
+      information is provided.
+    </p>
+
+    <p>
+      The data are contained in three files, <code>movies.dat</code>, 
+      <code>ratings.dat</code> and <code>tags.dat</code>.
+      Also included are scripts for generating subsets of the data to support five-fold
+      cross-validation of rating predictions.  More details about the contents and use
+      of all these files <a href="#file_desc">follows</a>.
+    </p>
+
+    <p>
+      This and other GroupLens data sets are publicly available for download at 
+      <a href="http://www.grouplens.org/taxonomy/term/14">GroupLens Data Sets</a>.
+    </p>
+    <h1>
+      Usage License
+    </h1>
+    <p>
+      Neither the University of Minnesota nor any of the researchers
+      involved can guarantee the correctness of the data, its suitability
+      for any particular purpose, or the validity of results based on the
+      use of the data set.  The data set may be used for any research
+      purposes under the following conditions:
+    </p>
+    <ul>
+        <li>The user may not state or imply any endorsement from the
+       University of Minnesota or the GroupLens Research Group.</li>
+
+        <li>The user must acknowledge the use of the data set in
+	publications resulting from the use of the data set (see below 
+	for citation information).</li>
+
+        <li>The user may not redistribute the data without separate
+       permission.</li>
+
+        <li>The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from a faculty member of the GroupLens Research Project at the
+       University of Minnesota.</li>
+    </ul>
+    <p>
+      The executable software scripts are provided "as is" without warranty 
+      of any kind, either expressed or implied, including, but not limited to, 
+      the implied warranties of merchantability and fitness for a particular purpose. 
+      The entire risk as to the quality and performance of them is with you. 
+      Should the program prove defective, you assume the cost of all 
+      necessary servicing, repair or correction.
+    </p>
+    <p>
+      In no event shall the University of Minnesota, its affiliates or employees 
+      be liable to you for any damages arising out of the use or inability to use 
+      these programs (including but not limited to loss of data or data being 
+      rendered inaccurate).
+    </p>
+
+    <p>
+      If you have any further questions or comments, please email <a href='mailto:grouplens-info@cs.umn.edu'>grouplens-info</a>
+    </p>
+
+    <h1>
+      Citation
+    </h1>
+    <p>
+      To acknowledge use of the dataset in publications, please cite the
+      following paper:
+    </p>
+    <p>
+      F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets:
+      History and Context. ACM Transactions on Interactive Intelligent
+      Systems (TiiS) 5, 4, Article 19 (December 2015), 19 pages.
+      DOI=<a href="http://dx.doi.org/10.1145/2827872">http://dx.doi.org/10.1145/2827872</a>
+    </p>
+
+    <h1>
+        Acknowledgements
+    </h1>
+    <p>
+      Thanks to Rich Davies for generating the data set.
+    </p>
+
+    <h1>
+        Further Information About GroupLens
+    </h1>
+    <p>
+      <a href="http://www.grouplens.org/">GroupLens</a> is a research group in the 
+      <a href="http://www.cs.umn.edu/">Department of Computer Science and Engineering</a>
+      at the <a href="http://www.umn.edu/">University of Minnesota</a>.  Since its 
+      inception in 1992, GroupLens' research projects have explored a variety of fields
+      including: 
+    </p>
+    <ul>
+      <li>Information Filtering</li>
+      <li>Recommender Systems</li>
+      <li>Online Communities</li>
+      <li>Mobile and Ubiquitious Technologies</li>
+      <li>Digital Libraries</li>
+      <li>Local Geographic Information Systems.</li>
+    </ul>
+    <p>
+      GroupLens Research operates a movie recommender based on
+      collaborative filtering, <a href="http://www.movielens.org/">MovieLens</a>,
+      which is the source of these data.
+    </p>
+
+    <h1 id="file_desc">
+      Content and Use of Files
+    </h1>
+
+    <h2>
+      Character Encoding
+    </h2>
+    <p>
+      The three data files are encoded as 
+      <a href="http://en.wikipedia.org/wiki/Utf-8">UTF-8</a>.  This is a departure
+      from previous MovieLens data sets, which used different character encodings.
+      If accented characters in movie titles or tag values (e.g. Misérables, Les (1995))
+      display incorrectly, make sure that any program reading the data, such as a 
+      text editor, terminal, or script, is configured for UTF-8.
+    </p>
+
+    <h2>
+      User Ids
+    </h2>
+    <p>
+      Movielens users were selected at random for inclusion.  Their ids have been 
+      anonymized.
+    </p>
+    <p>
+      Users were selected separately for inclusion 
+      in the ratings and tags data sets, which implies that user ids may appear in 
+      one set but not the other.
+    </p>
+    <p>
+      The anonymized values are consistent between the ratings and tags data files.  
+      That is, user id <em>n</em>, if it appears in both files, refers to the same
+      real MovieLens user.
+    </p>
+
+    <h2>
+      Ratings Data File Structure
+    </h2>
+    <p>
+      All ratings are contained in the file <code>ratings.dat</code>.  Each line of this 
+      file represents one rating of one movie by one user, and has the following format:
+    </p>
+    <p class="file_line_structure">
+      <code>UserID::MovieID::Rating::Timestamp</code>
+    </p>
+    <p>
+      The lines within this file are ordered first by UserID, then, within user, 
+      by MovieID.
+    </p>
+    <p>
+      Ratings are made on a 5-star scale, with half-star increments.
+    </p>
+    <p>
+      <a href="http://en.wikipedia.org/wiki/Unix_time">Timestamps</a> represent 
+      seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
+    </p>
+
+    <h2>
+      Tags Data File Structure
+    </h2>
+    <p>
+      All tags are contained in the file <code>tags.dat</code>.  Each line of this 
+      file represents one tag applied to one movie by one user, and has 
+      the following format:
+    </p>
+    <p class="file_line_structure">
+      <code>UserID::MovieID::Tag::Timestamp</code>
+    </p>
+    <p>
+      The lines within this file are ordered first by UserID, then, within user, 
+      by MovieID.
+    </p>
+    <p>
+      <a href="http://en.wikipedia.org/wiki/Tag_(metadata)">Tags</a> are user 
+      generated metadata about movies.  Each tag is typically a single word, or
+      short phrase.  The meaning, value and purpose of a particular tag is 
+      determined by each user.
+    </p>
+    <p>
+      <a href="http://en.wikipedia.org/wiki/Unix_time">Timestamps</a> represent 
+      seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
+    </p>
+
+    <h2>
+      Movies Data File Structure
+    </h2>
+    <p>
+      Movie information is contained in the file <code>movies.dat</code>.  
+      Each line of this file represents one movie, and has the following format:
+    </p>
+    <p class="file_line_structure">
+      <code>MovieID::Title::Genres</code>
+    </p>
+    <p>
+      MovieID is the real MovieLens id.
+    </p>
+    <p>
+      Movie titles, by policy, should be entered identically to those
+      found in <a href="http://www.imdb.com/">IMDB</a>, including year of release.
+      However, they are entered manually, so errors and inconsistencies may exist.
+    </p>
+    <p>
+      Genres are a pipe-separated list, and are selected from the following:
+    </p>
+    <ul>
+	  <li>Action</li>
+	  <li>Adventure</li>
+	  <li>Animation</li>
+	  <li>Children's</li>
+	  <li>Comedy</li>
+	  <li>Crime</li>
+	  <li>Documentary</li>
+	  <li>Drama</li>
+	  <li>Fantasy</li>
+	  <li>Film-Noir</li>
+	  <li>Horror</li>
+	  <li>Musical</li>
+	  <li>Mystery</li>
+	  <li>Romance</li>
+	  <li>Sci-Fi</li>
+	  <li>Thriller</li>
+	  <li>War</li>
+	  <li>Western</li>
+    </ul>
+
+    <h2>
+      Cross-Validation Subset Generation Scripts
+    </h2>
+    <p>
+      A Unix shell script, <code>split_ratings.sh</code>, is provided that, if desired, 
+      can be used to split the ratings data for five-fold cross-validation
+      of rating predictions.  It depends on a second script, allbut.pl, which 
+      is also included and is written in Perl.  They should run without modification
+      under Linux, Mac OS X, Cygwin or other Unix like systems.
+    </p>
+    <p>
+      Running <code>split_ratings.sh</code> will use <code>ratings.dat</code>
+      as input, and produce the fourteen output files described below.  Multiple
+      runs of the script will produce identical results.
+    </p>
+    <table style="width:75%" border="1">
+      <tr>
+        <th style="width:25%">File Names</th>
+        <th>Description</th>
+      </tr>
+      <tr>
+        <td>
+          r1.train, r2.train, r3.train, r4.train, r5.train<br/>
+          r1.test, r2.test, r3.test, r4.test, r5.test<br/>
+         </td>
+         <td>
+           The data sets r1.train and r1.test through r5.train and r5.test
+           are 80%/20% splits of the ratings data into training and test data.
+           Each of r1, ..., r5 have disjoint test sets; this if for
+           5 fold cross validation (where you repeat your experiment
+           with each training and test set and average the results).
+        </td>
+      </tr>
+      <tr>
+        <td>
+          ra.train, rb.train<br/>
+          ra.test, rb.test<br/>
+         </td>
+        <td>
+          The data sets ra.train, ra.test, rb.train, and rb.test
+          split the ratings data into a training set and a test set with
+          exactly 10 ratings per user in the test set.  The sets
+          ra.test and rb.test are disjoint.
+         </td>
+      </tr>
+    </table>
+    <p style="text-align:right">
+      <a href="http://validator.w3.org/check?uri=referer">
+        <img style="border:0;width:88px;height:31px"
+          src="http://www.w3.org/Icons/valid-xhtml10"
+          alt="Valid XHTML 1.0 Strict" height="31" width="88" />
+      </a>
+
+      <a href="http://jigsaw.w3.org/css-validator/">
+        <img style="border:0;width:88px;height:31px"
+          src="http://jigsaw.w3.org/css-validator/images/vcss"
+          alt="Valid CSS!" />
+      </a>
+    </p>
+  </body>
+</html>
+

movielens_10m/ml-10M100K/allbut.pl

@@ -0,0 +1,35 @@
+#!/usr/bin/env perl
+
+# get args
+if (@ARGV < 3) {
+	print STDERR "Usage: $0 base_name start stop max_test [ratings ...]\n";
+	exit 1;
+}
+$basename = shift;
+$start = shift;
+$stop = shift;
+$maxtest = shift;
+
+# open files
+open( TESTFILE, ">$basename.test" ) or die "Cannot open $basename.test for writing\n";
+open( BASEFILE, ">$basename.train" ) or die "Cannot open $basename.train for writing\n";
+
+# init variables
+$testcnt = 0;
+
+while (<>) {
+	($user) = split /::/, $_, 2;
+	if (! defined $ratingcnt{$user}) {
+		$ratingcnt{$user} = 1;
+	} else {
+	        ++$ratingcnt{$user};
+        }
+	if (($testcnt < $maxtest || $maxtest <= 0)
+	&& $ratingcnt{$user} >= $start && $ratingcnt{$user} <= $stop) {
+		++$testcnt;
+		print TESTFILE;
+	}
+	else {
+		print BASEFILE;
+	}
+}

movielens_10m/ml-10M100K/split_ratings.sh

@@ -0,0 +1,36 @@
+#!/bin/sh
+
+RATINGS_COUNT=`wc -l ratings.dat | xargs | cut -d ' ' -f 1`
+echo "ratings count: $RATINGS_COUNT"
+SET_SIZE=`expr $RATINGS_COUNT / 5`
+echo "set size: $SET_SIZE"
+REMAINDER=`expr $RATINGS_COUNT % 5`
+echo "remainder: $REMAINDER"
+
+for i in 1 2 3 4 5
+  do
+    head -`expr $i \* $SET_SIZE` ratings.dat | tail -$SET_SIZE > r$i.test
+
+    # XXX: OSX users will see the message "head: illegal line count -- 0" here,
+    #      but this is just a warning; the script still works as intended.
+    head -`expr \( $i - 1 \) \* $SET_SIZE` ratings.dat > r$i.train
+    tail -`expr \( 5 - $i \) \* $SET_SIZE` ratings.dat >> r$i.train
+
+    if [ $i -eq 5 ]; then
+       tail -$REMAINDER ratings.dat >> r5.test
+    else
+       tail -$REMAINDER ratings.dat >> r$i.train
+    fi
+
+    echo "r$i.test created.  `wc -l r$i.test | xargs | cut -d " " -f 1` lines."
+    echo "r$i.train created.  `wc -l r$i.train | xargs | cut -d " " -f 1` lines."
+done
+
+./allbut.pl ra 1 10 0 ratings.dat
+echo "ra.test created.  `wc -l ra.test | xargs | cut -d " " -f 1` lines."
+echo "ra.train created.  `wc -l ra.train | xargs | cut -d " " -f 1` lines."
+
+./allbut.pl rb 11 20 0 ratings.dat
+echo "rb.test created.  `wc -l rb.test | xargs | cut -d " " -f 1` lines."
+echo "rb.train created.  `wc -l rb.train | xargs | cut -d " " -f 1` lines."
+

movielens_1m/ml-1m/README

@@ -0,0 +1,170 @@
+SUMMARY
+================================================================================
+
+These files contain 1,000,209 anonymous ratings of approximately 3,900 movies 
+made by 6,040 MovieLens users who joined MovieLens in 2000.
+
+USAGE LICENSE
+================================================================================
+
+Neither the University of Minnesota nor any of the researchers
+involved can guarantee the correctness of the data, its suitability
+for any particular purpose, or the validity of results based on the
+use of the data set.  The data set may be used for any research
+purposes under the following conditions:
+
+     * The user may not state or imply any endorsement from the
+       University of Minnesota or the GroupLens Research Group.
+
+     * The user must acknowledge the use of the data set in
+       publications resulting from the use of the data set
+       (see below for citation information).
+
+     * The user may not redistribute the data without separate
+       permission.
+
+     * The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from a faculty member of the GroupLens Research Project at the
+       University of Minnesota.
+
+If you have any further questions or comments, please contact GroupLens
+<grouplens-info@cs.umn.edu>. 
+
+CITATION
+================================================================================
+
+To acknowledge use of the dataset in publications, please cite the following
+paper:
+
+F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History
+and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4,
+Article 19 (December 2015), 19 pages. DOI=http://dx.doi.org/10.1145/2827872
+
+
+ACKNOWLEDGEMENTS
+================================================================================
+
+Thanks to Shyong Lam and Jon Herlocker for cleaning up and generating the data
+set.
+
+FURTHER INFORMATION ABOUT THE GROUPLENS RESEARCH PROJECT
+================================================================================
+
+The GroupLens Research Project is a research group in the Department of 
+Computer Science and Engineering at the University of Minnesota. Members of 
+the GroupLens Research Project are involved in many research projects related 
+to the fields of information filtering, collaborative filtering, and 
+recommender systems. The project is lead by professors John Riedl and Joseph 
+Konstan. The project began to explore automated collaborative filtering in 
+1992, but is most well known for its world wide trial of an automated 
+collaborative filtering system for Usenet news in 1996. Since then the project 
+has expanded its scope to research overall information filtering solutions, 
+integrating in content-based methods as well as improving current collaborative 
+filtering technology.
+
+Further information on the GroupLens Research project, including research 
+publications, can be found at the following web site:
+        
+        http://www.grouplens.org/
+
+GroupLens Research currently operates a movie recommender based on 
+collaborative filtering:
+
+        http://www.movielens.org/
+
+RATINGS FILE DESCRIPTION
+================================================================================
+
+All ratings are contained in the file "ratings.dat" and are in the
+following format:
+
+UserID::MovieID::Rating::Timestamp
+
+- UserIDs range between 1 and 6040 
+- MovieIDs range between 1 and 3952
+- Ratings are made on a 5-star scale (whole-star ratings only)
+- Timestamp is represented in seconds since the epoch as returned by time(2)
+- Each user has at least 20 ratings
+
+USERS FILE DESCRIPTION
+================================================================================
+
+User information is in the file "users.dat" and is in the following
+format:
+
+UserID::Gender::Age::Occupation::Zip-code
+
+All demographic information is provided voluntarily by the users and is
+not checked for accuracy.  Only users who have provided some demographic
+information are included in this data set.
+
+- Gender is denoted by a "M" for male and "F" for female
+- Age is chosen from the following ranges:
+
+	*  1:  "Under 18"
+	* 18:  "18-24"
+	* 25:  "25-34"
+	* 35:  "35-44"
+	* 45:  "45-49"
+	* 50:  "50-55"
+	* 56:  "56+"
+
+- Occupation is chosen from the following choices:
+
+	*  0:  "other" or not specified
+	*  1:  "academic/educator"
+	*  2:  "artist"
+	*  3:  "clerical/admin"
+	*  4:  "college/grad student"
+	*  5:  "customer service"
+	*  6:  "doctor/health care"
+	*  7:  "executive/managerial"
+	*  8:  "farmer"
+	*  9:  "homemaker"
+	* 10:  "K-12 student"
+	* 11:  "lawyer"
+	* 12:  "programmer"
+	* 13:  "retired"
+	* 14:  "sales/marketing"
+	* 15:  "scientist"
+	* 16:  "self-employed"
+	* 17:  "technician/engineer"
+	* 18:  "tradesman/craftsman"
+	* 19:  "unemployed"
+	* 20:  "writer"
+
+MOVIES FILE DESCRIPTION
+================================================================================
+
+Movie information is in the file "movies.dat" and is in the following
+format:
+
+MovieID::Title::Genres
+
+- Titles are identical to titles provided by the IMDB (including
+year of release)
+- Genres are pipe-separated and are selected from the following genres:
+
+	* Action
+	* Adventure
+	* Animation
+	* Children's
+	* Comedy
+	* Crime
+	* Documentary
+	* Drama
+	* Fantasy
+	* Film-Noir
+	* Horror
+	* Musical
+	* Mystery
+	* Romance
+	* Sci-Fi
+	* Thriller
+	* War
+	* Western
+
+- Some MovieIDs do not correspond to a movie due to accidental duplicate
+entries and/or test entries
+- Movies are mostly entered by hand, so errors and inconsistencies may exist

movielens_25m/ml-25m/README.txt

@@ -0,0 +1,195 @@
+Summary
+=======
+
+This dataset (ml-25m) describes 5-star rating and free-text tagging activity from [MovieLens](http://movielens.org), a movie recommendation service. It contains 25000095 ratings and 1093360 tag applications across 62423 movies. These data were created by 162541 users between January 09, 1995 and November 21, 2019. This dataset was generated on November 21, 2019.
+
+Users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided.
+
+The data are contained in the files `genome-scores.csv`, `genome-tags.csv`, `links.csv`, `movies.csv`, `ratings.csv` and `tags.csv`. More details about the contents and use of all these files follows.
+
+This and other GroupLens data sets are publicly available for download at <http://grouplens.org/datasets/>.
+
+
+Usage License
+=============
+
+Neither the University of Minnesota nor any of the researchers involved can guarantee the correctness of the data, its suitability for any particular purpose, or the validity of results based on the use of the data set. The data set may be used for any research purposes under the following conditions:
+
+* The user may not state or imply any endorsement from the University of Minnesota or the GroupLens Research Group.
+* The user must acknowledge the use of the data set in publications resulting from the use of the data set (see below for citation information).
+* The user may not redistribute the data without separate permission.
+* The user may not use this information for any commercial or revenue-bearing purposes without first obtaining permission from a faculty member of the GroupLens Research Project at the University of Minnesota.
+* The executable software scripts are provided "as is" without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the quality and performance of them is with you. Should the program prove defective, you assume the cost of all necessary servicing, repair or correction.
+
+In no event shall the University of Minnesota, its affiliates or employees be liable to you for any damages arising out of the use or inability to use these programs (including but not limited to loss of data or data being rendered inaccurate).
+
+If you have any further questions or comments, please email <grouplens-info@umn.edu>
+
+
+Citation
+========
+
+To acknowledge use of the dataset in publications, please cite the following paper:
+
+> F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. <https://doi.org/10.1145/2827872>
+
+
+Further Information About GroupLens
+===================================
+
+GroupLens is a research group in the Department of Computer Science and Engineering at the University of Minnesota. Since its inception in 1992, GroupLens's research projects have explored a variety of fields including:
+
+* recommender systems
+* online communities
+* mobile and ubiquitious technologies
+* digital libraries
+* local geographic information systems
+
+GroupLens Research operates a movie recommender based on collaborative filtering, MovieLens, which is the source of these data. We encourage you to visit <http://movielens.org> to try it out! If you have exciting ideas for experimental work to conduct on MovieLens, send us an email at <grouplens-info@cs.umn.edu> - we are always interested in working with external collaborators.
+
+
+Content and Use of Files
+========================
+
+Verifying the Dataset Contents
+------------------------------
+
+We encourage you to verify that the dataset you have on your computer is identical to the ones hosted at [grouplens.org](http://grouplens.org).  This is an important step if you downloaded the dataset from a location other than [grouplens.org](http://grouplens.org), or if you wish to publish research results based on analysis of the MovieLens dataset.
+
+We provide a [MD5 checksum](http://en.wikipedia.org/wiki/Md5sum) with the same name as the downloadable `.zip` file, but with a `.md5` file extension. To verify the dataset:
+
+    # on linux
+    md5sum ml-25m.zip; cat ml-25m.zip.md5
+
+    # on OSX
+    md5 ml-25m.zip; cat ml-25m.zip.md5
+
+    # windows users can download a tool from Microsoft (or elsewhere) that verifies MD5 checksums
+
+Check that the two lines of output contain the same hash value.
+
+
+Formatting and Encoding
+-----------------------
+
+The dataset files are written as [comma-separated values](http://en.wikipedia.org/wiki/Comma-separated_values) files with a single header row. Columns that contain commas (`,`) are escaped using double-quotes (`"`). These files are encoded as UTF-8. If accented characters in movie titles or tag values (e.g. Misérables, Les (1995)) display incorrectly, make sure that any program reading the data, such as a text editor, terminal, or script, is configured for UTF-8.
+
+
+User Ids
+--------
+
+MovieLens users were selected at random for inclusion. Their ids have been anonymized. User ids are consistent between `ratings.csv` and `tags.csv` (i.e., the same id refers to the same user across the two files).
+
+
+Movie Ids
+---------
+
+Only movies with at least one rating or tag are included in the dataset. These movie ids are consistent with those used on the MovieLens web site (e.g., id `1` corresponds to the URL <https://movielens.org/movies/1>). Movie ids are consistent between `ratings.csv`, `tags.csv`, `movies.csv`, and `links.csv` (i.e., the same id refers to the same movie across these four data files).
+
+
+Ratings Data File Structure (ratings.csv)
+-----------------------------------------
+
+All ratings are contained in the file `ratings.csv`. Each line of this file after the header row represents one rating of one movie by one user, and has the following format:
+
+    userId,movieId,rating,timestamp
+
+The lines within this file are ordered first by userId, then, within user, by movieId.
+
+Ratings are made on a 5-star scale, with half-star increments (0.5 stars - 5.0 stars).
+
+Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
+
+
+Tags Data File Structure (tags.csv)
+-----------------------------------
+
+All tags are contained in the file `tags.csv`. Each line of this file after the header row represents one tag applied to one movie by one user, and has the following format:
+
+    userId,movieId,tag,timestamp
+
+The lines within this file are ordered first by userId, then, within user, by movieId.
+
+Tags are user-generated metadata about movies. Each tag is typically a single word or short phrase. The meaning, value, and purpose of a particular tag is determined by each user.
+
+Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
+
+
+Movies Data File Structure (movies.csv)
+---------------------------------------
+
+Movie information is contained in the file `movies.csv`. Each line of this file after the header row represents one movie, and has the following format:
+
+    movieId,title,genres
+
+Movie titles are entered manually or imported from <https://www.themoviedb.org/>, and include the year of release in parentheses. Errors and inconsistencies may exist in these titles.
+
+Genres are a pipe-separated list, and are selected from the following:
+
+* Action
+* Adventure
+* Animation
+* Children's
+* Comedy
+* Crime
+* Documentary
+* Drama
+* Fantasy
+* Film-Noir
+* Horror
+* Musical
+* Mystery
+* Romance
+* Sci-Fi
+* Thriller
+* War
+* Western
+* (no genres listed)
+
+
+Links Data File Structure (links.csv)
+---------------------------------------
+
+Identifiers that can be used to link to other sources of movie data are contained in the file `links.csv`. Each line of this file after the header row represents one movie, and has the following format:
+
+    movieId,imdbId,tmdbId
+
+movieId is an identifier for movies used by <https://movielens.org>. E.g., the movie Toy Story has the link <https://movielens.org/movies/1>.
+
+imdbId is an identifier for movies used by <http://www.imdb.com>. E.g., the movie Toy Story has the link <http://www.imdb.com/title/tt0114709/>.
+
+tmdbId is an identifier for movies used by <https://www.themoviedb.org>. E.g., the movie Toy Story has the link <https://www.themoviedb.org/movie/862>.
+
+Use of the resources listed above is subject to the terms of each provider.
+
+
+Tag Genome (genome-scores.csv and genome-tags.csv)
+-------------------------------------------------
+
+This data set includes a current copy of the Tag Genome.
+
+[genome-paper]: http://files.grouplens.org/papers/tag_genome.pdf
+
+The tag genome is a data structure that contains tag relevance scores for movies.  The structure is a dense matrix: each movie in the genome has a value for *every* tag in the genome.
+
+As described in [this article][genome-paper], the tag genome encodes how strongly movies exhibit particular properties represented by tags (atmospheric, thought-provoking, realistic, etc.). The tag genome was computed using a machine learning algorithm on user-contributed content including tags, ratings, and textual reviews.
+
+The genome is split into two files.  The file `genome-scores.csv` contains movie-tag relevance data in the following format:
+
+    movieId,tagId,relevance
+
+The second file, `genome-tags.csv`, provides the tag descriptions for the tag IDs in the genome file, in the following format:
+
+    tagId,tag
+
+The `tagId` values are generated when the data set is exported, so they may vary from version to version of the MovieLens data sets.
+
+Please include the following citation if referencing tag genome data:
+
+> Jesse Vig, Shilad Sen, and John Riedl. 2012. The Tag Genome: Encoding Community Knowledge to Support Novel Interaction. ACM Trans. Interact. Intell. Syst. 2, 3: 13:1–13:44. <https://doi.org/10.1145/2362394.2362395>
+
+
+Cross-Validation
+----------------
+
+Prior versions of the MovieLens dataset included either pre-computed cross-folds or scripts to perform this computation. We no longer bundle either of these features with the dataset, since most modern toolkits provide this as a built-in feature. If you wish to learn about standard approaches to cross-fold computation in the context of recommender systems evaluation, see [LensKit](http://lenskit.org) for tools, documentation, and open-source code examples.

movielens_25m/ml-25m/genome-tags.csv

@@ -0,0 +1,1129 @@
+tagId,tag
+1,007
+2,007 (series)
+3,18th century
+4,1920s
+5,1930s
+6,1950s
+7,1960s
+8,1970s
+9,1980s
+10,19th century
+11,3d
+12,70mm
+13,80s
+14,9/11
+15,aardman
+16,aardman studios
+17,abortion
+18,absurd
+19,action
+20,action packed
+21,adaptation
+22,adapted from:book
+23,adapted from:comic
+24,adapted from:game
+25,addiction
+26,adolescence
+27,adoption
+28,adultery
+29,adventure
+30,affectionate
+31,afi 100
+32,afi 100 (laughs)
+33,afi 100 (movie quotes)
+34,africa
+35,afterlife
+36,aging
+37,aids
+38,airplane
+39,airport
+40,alaska
+41,alcatraz
+42,alcoholism
+43,alien
+44,alien invasion
+45,aliens
+46,allegory
+47,almodovar
+48,alone in the world
+49,alter ego
+50,alternate endings
+51,alternate history
+52,alternate reality
+53,alternate universe
+54,amazing cinematography
+55,amazing photography
+56,american civil war
+57,amnesia
+58,amy smart
+59,android(s)/cyborg(s)
+60,androids
+61,animal movie
+62,animals
+63,animated
+64,animation
+65,anime
+66,antarctica
+67,anti-hero
+68,anti-semitism
+69,anti-war
+70,apocalypse
+71,archaeology
+72,argentina
+73,arms dealer
+74,arnold
+75,art
+76,art house
+77,artificial intelligence
+78,artist
+79,artistic
+80,artsy
+81,assassin
+82,assassination
+83,assassins
+84,astronauts
+85,atheism
+86,atmospheric
+87,australia
+88,australian
+89,author:alan moore
+90,author:neil gaiman
+91,autism
+92,aviation
+93,awesome
+94,awesome soundtrack
+95,awful
+96,bad
+97,bad acting
+98,bad cgi
+99,bad ending
+100,bad plot
+101,bad science
+102,bad script
+103,bad sequel
+104,ballet
+105,bank robbery
+106,baseball
+107,based on a book
+108,based on a comic
+109,based on a play
+110,based on a true story
+111,based on a tv show
+112,based on a video game
+113,based on book
+114,based on comic
+115,based on true story
+116,basketball
+117,batman
+118,bdsm
+119,beatles
+120,beautiful
+121,beautiful scenery
+122,beautifully filmed
+123,beauty pageant
+124,beer
+125,berlin
+126,best of 2005
+127,best war films
+128,betrayal
+129,better than expected
+130,better than the american version
+131,biblical
+132,big budget
+133,biographical
+134,biography
+135,biopic
+136,birds
+137,biting
+138,bittersweet
+139,bizarre
+140,black and white
+141,black comedy
+142,blaxploitation
+143,bleak
+144,blindness
+145,blood
+146,bloody
+147,boarding school
+148,boat
+149,bollywood
+150,bombs
+151,bond
+152,book
+153,book was better
+154,books
+155,boring
+156,boring!
+157,boston
+158,bowling
+159,boxing
+160,brainwashing
+161,brazil
+162,breathtaking
+163,brilliant
+164,british
+165,british comedy
+166,broadway
+167,brothers
+168,brutal
+169,brutality
+170,buddy movie
+171,bullshit history
+172,bullying
+173,business
+174,c.s. lewis
+175,california
+176,camp
+177,campy
+178,canada
+179,cancer
+180,cannibalism
+181,caper
+182,capitalism
+183,car chase
+184,carrie-anne moss
+185,cars
+186,cartoon
+187,casino
+188,catastrophe
+189,cathartic
+190,catholicism
+191,censorship
+192,cerebral
+193,cgi
+194,character study
+195,chase
+196,cheating
+197,cheerleading
+198,cheesy
+199,chess
+200,chicago
+201,chick flick
+202,child abuse
+203,childhood
+204,children
+205,china
+206,chocolate
+207,chris tucker
+208,christian
+209,christianity
+210,christmas
+211,cia
+212,cinematography
+213,circus
+214,civil war
+215,classic
+216,classic car
+217,classical music
+218,claustrophobic
+219,claymation
+220,clever
+221,clones
+222,cloning
+223,clowns
+224,coen bros
+225,coen brothers
+226,cold war
+227,college
+228,colonialism
+229,colourful
+230,comedy
+231,comic
+232,comic book
+233,comic book adaption
+234,comics
+235,coming of age
+236,coming-of-age
+237,communism
+238,compassionate
+239,competition
+240,complex
+241,complex characters
+242,complicated
+243,complicated plot
+244,computer animation
+245,computer game
+246,computers
+247,con artists
+248,con men
+249,confrontational
+250,confusing
+251,conspiracy
+252,conspiracy theory
+253,controversial
+254,cooking
+255,cool
+256,corny
+257,corporate america
+258,corruption
+259,costume drama
+260,courage
+261,court
+262,courtroom
+263,courtroom drama
+264,crappy sequel
+265,crazy
+266,creativity
+267,creepy
+268,crime
+269,crime gone awry
+270,criterion
+271,cross dressing
+272,crude humor
+273,cuba
+274,cult
+275,cult classic
+276,cult film
+277,culture clash
+278,cute
+279,cute!
+280,cyberpunk
+281,cyborgs
+282,cynical
+283,dance
+284,dancing
+285,dark
+286,dark comedy
+287,dark fantasy
+288,dark hero
+289,dark humor
+290,dc comics
+291,deadpan
+292,death
+293,death penalty
+294,demons
+295,depp & burton
+296,depressing
+297,depression
+298,desert
+299,destiny
+300,detective
+301,devil
+302,dialogue
+303,dialogue driven
+304,dinosaurs
+305,directorial debut
+306,disability
+307,disappointing
+308,disaster
+309,disney
+310,disney animated feature
+311,distopia
+312,disturbing
+313,divorce
+314,doctors
+315,documentary
+316,dog
+317,dogs
+318,dolphins
+319,downbeat
+320,dr. seuss
+321,dragon
+322,dragons
+323,drama
+324,dramatic
+325,dreamlike
+326,dreams
+327,dreamworks
+328,drinking
+329,drug abuse
+330,drug addiction
+331,drugs
+332,dumb
+333,dumb but funny
+334,dynamic cgi action
+335,dysfunctional family
+336,dystopia
+337,dystopic future
+338,earnest
+339,easily confused with other movie(s) (title)
+340,east germany
+341,eccentricity
+342,ecology
+343,educational
+344,eerie
+345,effects
+346,egypt
+347,emma watson
+348,emotional
+349,end of the world
+350,england
+351,enigmatic
+352,ennio morricone
+353,enormously long battle scene
+354,ensemble cast
+355,entertaining
+356,entirely dialogue
+357,environment
+358,environmental
+359,epic
+360,erotic
+361,espionage
+362,ethnic conflict
+363,evolution
+364,excellent
+365,excellent script
+366,exceptional acting
+367,exciting
+368,existentialism
+369,explosions
+370,factual
+371,fairy tale
+372,fairy tales
+373,fake documentary
+374,family
+375,family bonds
+376,family drama
+377,fantasy
+378,fantasy world
+379,farce
+380,fascism
+381,fashion
+382,fast paced
+383,father daughter relationship
+384,father son relationship
+385,father-son relationship
+386,fbi
+387,feel good movie
+388,feel-good
+389,fight scenes
+390,fighting
+391,fighting the system
+392,figure skating
+393,film noir
+394,finnish
+395,firefly
+396,first contact
+397,fish
+398,flashbacks
+399,food
+400,football
+401,forceful
+402,foreign
+403,foul language
+404,fountain of youth
+405,france
+406,franchise
+407,francis ford copolla
+408,free speech
+409,free to download
+410,freedom
+411,french
+412,friendship
+413,frightening
+414,fun
+415,fun movie
+416,funniest movies
+417,funny
+418,funny as hell
+419,future
+420,futuristic
+421,gambling
+422,gangs
+423,gangster
+424,gangsters
+425,gay
+426,gay character
+427,geek
+428,geeks
+429,genetics
+430,genius
+431,genocide
+432,george orwell
+433,german
+434,germany
+435,ghosts
+436,ghosts/afterlife
+437,giant robots
+438,gilliam
+439,girlie movie
+440,glbt
+441,global warming
+442,god
+443,golden palm
+444,golf
+445,good
+446,good acting
+447,good action
+448,good dialogue
+449,good music
+450,good romantic comedies
+451,good sequel
+452,good soundtrack
+453,good story
+454,good versus evil
+455,goofy
+456,gore
+457,goretastic
+458,gory
+459,goth
+460,gothic
+461,graphic design
+462,graphic novel
+463,gratuitous violence
+464,great
+465,great acting
+466,great cinematography
+467,great dialogue
+468,great ending
+469,great movie
+470,great music
+471,great soundtrack
+472,greed
+473,grim
+474,grindhouse
+475,gritty
+476,gross-out
+477,gruesome
+478,guilt
+479,guilty pleasure
+480,gulf war
+481,gunfight
+482,guns
+483,gypsy accent
+484,hackers
+485,hacking
+486,halloween
+487,hallucinatory
+488,handycam
+489,hannibal lecter
+490,happy ending
+491,hard to watch
+492,harry potter
+493,harsh
+494,haunted house
+495,hawaii
+496,heartbreaking
+497,heartwarming
+498,heist
+499,heroin
+500,heroine
+501,heroine in tight suit
+502,high fantasy
+503,high school
+504,highly quotable
+505,hilarious
+506,hillarious
+507,hip hop
+508,historical
+509,history
+510,hit men
+511,hitchcock
+512,hitman
+513,holiday
+514,hollywood
+515,holocaust
+516,homeless
+517,homophobia
+518,homosexuality
+519,honest
+520,hong kong
+521,horrible
+522,horror
+523,horses
+524,hospital
+525,hostage
+526,hotel
+527,humanity
+528,humor
+529,humorous
+530,hunting
+531,idealism
+532,identity
+533,idiotic
+534,imaginary friend
+535,imagination
+536,imdb top 250
+537,immigrants
+538,immortality
+539,incest
+540,independent film
+541,india
+542,indiana jones
+543,indians
+544,indie
+545,infidelity
+546,innocence lost
+547,insanity
+548,inspirational
+549,inspiring
+550,intellectual
+551,intelligent
+552,intelligent sci-fi
+553,intense
+554,interesting
+555,internet
+556,interracial romance
+557,intimate
+558,investigation
+559,iran
+560,iraq
+561,iraq war
+562,ireland
+563,irish
+564,irish accent
+565,ironic
+566,irreverent
+567,islam
+568,island
+569,isolation
+570,israel
+571,italian
+572,italy
+573,james bond
+574,jane austen
+575,japan
+576,japanese
+577,jay and silent bob
+578,jazz
+579,jesus
+580,jewish
+581,jews
+582,journalism
+583,judaism
+584,jungle
+585,justice
+586,kick-butt women
+587,kidnapping
+588,kids
+589,kids and family
+590,king arthur
+591,kubrick
+592,kung fu
+593,kurosawa
+594,lame
+595,las vegas
+596,latin america
+597,lawyer
+598,lawyers
+599,lesbian
+600,life
+601,life & death
+602,life philosophy
+603,light
+604,lions
+605,literary adaptation
+606,literature
+607,liv tyler
+608,london
+609,lone hero
+610,loneliness
+611,long
+612,los angeles
+613,love
+614,love story
+615,love triangles
+616,low budget
+617,lynch
+618,lyrical
+619,macabre
+620,mad scientist
+621,made for tv
+622,mafia
+623,magic
+624,magic realism
+625,male nudity
+626,man versus machine
+627,manipulation
+628,marijuana
+629,marriage
+630,mars
+631,martial arts
+632,marvel
+633,marx brothers
+634,masterpiece
+635,math
+636,mathematics
+637,maze
+638,medieval
+639,meditative
+640,melancholic
+641,melancholy
+642,memory
+643,memory loss
+644,mental hospital
+645,mental illness
+646,mentor
+647,metaphysics
+648,mexico
+649,middle east
+650,midlife crisis
+651,military
+652,mindfuck
+653,mining
+654,minnesota
+655,mission from god
+656,mistaken identity
+657,miyazaki
+658,mob
+659,mockumentary
+660,modern fantasy
+661,money
+662,monkey
+663,monster
+664,monsters
+665,monty python
+666,moody
+667,moon
+668,moral ambiguity
+669,morality
+670,mother daughter relationship
+671,mother-son relationship
+672,motorcycle
+673,mountain climbing
+674,movie business
+675,movielens top pick
+676,moving
+677,mozart
+678,mtv
+679,multiple storylines
+680,mummy
+681,muppets
+682,murder
+683,murder mystery
+684,music
+685,music business
+686,musical
+687,musicians
+688,mutants
+689,mystery
+690,mythology
+691,narrated
+692,nasa
+693,native americans
+694,natural disaster
+695,nature
+696,nazi
+697,nazis
+698,neil gaiman
+699,neo-nazis
+700,neo-noir
+701,nerds
+702,new jersey
+703,new orleans
+704,new york
+705,new york city
+706,new zealand
+707,ninja
+708,no dialogue
+709,no plot
+710,nocturnal
+711,noir
+712,noir thriller
+713,non-hollywood ending
+714,non-linear
+715,nonlinear
+716,nostalgia
+717,nostalgic
+718,not as good as the first
+719,not funny
+720,notable nudity
+721,notable soundtrack
+722,nuclear
+723,nuclear bomb
+724,nuclear war
+725,nudity
+726,nudity (full frontal - brief)
+727,nudity (full frontal - notable)
+728,nudity (full frontal)
+729,nudity (rear)
+730,nudity (topless - brief)
+731,nudity (topless - notable)
+732,nudity (topless)
+733,obsession
+734,ocean
+735,off-beat comedy
+736,office
+737,oil
+738,olympics
+739,ominous
+740,opera
+741,organized crime
+742,original
+743,original plot
+744,orphans
+745,oscar
+746,oscar (best actor)
+747,oscar (best actress)
+748,oscar (best animated feature)
+749,oscar (best cinematography)
+750,oscar (best directing)
+751,oscar (best editing)
+752,oscar (best effects - visual effects)
+753,oscar (best foreign language film)
+754,oscar (best music - original score)
+755,oscar (best music - original song)
+756,oscar (best picture)
+757,oscar (best sound)
+758,oscar (best supporting actor)
+759,oscar (best supporting actress)
+760,oscar (best writing - screenplay written directly for the screen)
+761,oscar winner
+762,over the top
+763,overrated
+764,palestine
+765,parallel universe
+766,paranoia
+767,paranoid
+768,parenthood
+769,paris
+770,parody
+771,passionate
+772,penguins
+773,perfect
+774,period piece
+775,peter pan
+776,pg
+777,pg-13
+778,philip k. dick
+779,philosophical
+780,philosophy
+781,photographer
+782,photography
+783,pigs
+784,pirates
+785,pixar
+786,pixar animation
+787,plot
+788,plot holes
+789,plot twist
+790,poetry
+791,poignant
+792,pointless
+793,poker
+794,poland
+795,police
+796,police corruption
+797,police investigation
+798,political
+799,political corruption
+800,politics
+801,pornography
+802,post apocalyptic
+803,post-apocalyptic
+804,potential oscar nom
+805,poverty
+806,powerful ending
+807,predictable
+808,pregnancy
+809,prejudice
+810,prequel
+811,president
+812,pretentious
+813,prison
+814,prison escape
+815,private detective
+816,product placement
+817,prohibition
+818,propaganda
+819,prostitution
+820,psychedelic
+821,psychiatrist
+822,psychiatry
+823,psychological
+824,psychology
+825,pulp
+826,punk
+827,puppets
+828,queer
+829,quirky
+830,quotable
+831,rabbits
+832,race
+833,race issues
+834,racing
+835,racism
+836,radio
+837,rags to riches
+838,rape
+839,rats
+840,realistic
+841,realistic action
+842,reality tv
+843,rebellion
+844,redemption
+845,reflective
+846,relationships
+847,religion
+848,remade
+849,remake
+850,revenge
+851,revolution
+852,ridiculous
+853,rio de janeiro
+854,road movie
+855,road trip
+856,roald dahl
+857,robbery
+858,robert downey jr
+859,robert ludlum
+860,robot
+861,robots
+862,rock and roll
+863,romance
+864,romantic
+865,romantic comedy
+866,rome
+867,runaway
+868,russia
+869,russian
+870,sacrifice
+871,sad
+872,sad but good
+873,samurai
+874,san francisco
+875,sappy
+876,sarcasm
+877,satire
+878,satirical
+879,saturday night live
+880,saturn award (best science fiction film)
+881,saturn award (best special effects)
+882,scary
+883,scenic
+884,schizophrenia
+885,school
+886,sci fi
+887,sci-fi
+888,science
+889,science fiction
+890,scifi
+891,scifi cult
+892,scotland
+893,screwball
+894,screwball comedy
+895,script
+896,secret service
+897,secrets
+898,segregation
+899,self discovery
+900,sentimental
+901,sequel
+902,sequels
+903,serial killer
+904,series
+905,sex
+906,sex comedy
+907,sexual
+908,sexual abuse
+909,sexuality
+910,sexualized violence
+911,sexy
+912,shakespeare
+913,shallow
+914,shark
+915,shopping
+916,short
+917,short-term memory loss
+918,silent
+919,silly
+920,silly fun
+921,simple
+922,single father
+923,sisters
+924,skinhead
+925,slackers
+926,slapstick
+927,slasher
+928,slavery
+929,slow
+930,slow paced
+931,small town
+932,snakes
+933,so bad it's funny
+934,so bad it's good
+935,soccer
+936,social commentary
+937,solitude
+938,sophia coppola
+939,south africa
+940,south america
+941,southern theme
+942,space
+943,space opera
+944,space program
+945,space travel
+946,spaghetti western
+947,spain
+948,spanish
+949,spanish civil war
+950,special
+951,special effects
+952,spelling bee
+953,spiders
+954,spielberg
+955,spies
+956,splatter
+957,spock
+958,spoof
+959,sports
+960,spy
+961,spying
+962,stage magic
+963,stand-up comedy
+964,star trek
+965,star wars
+966,steampunk
+967,stereotypes
+968,stoner movie
+969,stop motion
+970,stop-motion
+971,story
+972,storytelling
+973,stranded
+974,strange
+975,strippers
+976,studio ghibli
+977,stunning
+978,stupid
+979,stupid as hell
+980,stupidity
+981,stylish
+982,stylized
+983,submarine
+984,suburbia
+985,suicide
+986,suicide attempt
+987,super hero
+988,super-hero
+989,superhero
+990,superheroes
+991,supernatural
+992,suprisingly clever
+993,surfing
+994,surprise ending
+995,surreal
+996,surrealism
+997,surveillance
+998,survival
+999,suspense
+1000,suspenseful
+1001,swashbuckler
+1002,swedish
+1003,sweet
+1004,switching places
+1005,sword fight
+1006,sword fighting
+1007,talking animals
+1008,talky
+1009,tarantino
+1010,teacher
+1011,tear jerker
+1012,technology
+1013,teen
+1014,teen movie
+1015,teenager
+1016,teenagers
+1017,teens
+1018,teleportation
+1019,television
+1020,tense
+1021,terminal illness
+1022,terrorism
+1023,texas
+1024,thought-provoking
+1025,thriller
+1026,time
+1027,time loop
+1028,time travel
+1029,tokyo
+1030,tolkien
+1031,tom clancy
+1032,too long
+1033,too short
+1034,torture
+1035,touching
+1036,toys
+1037,tragedy
+1038,train
+1039,trains
+1040,transformation
+1041,transgender
+1042,travel
+1043,treasure
+1044,treasure hunt
+1045,tricky
+1046,trilogy
+1047,true story
+1048,truman capote
+1049,twist
+1050,twist ending
+1051,twists & turns
+1052,undercover cop
+1053,underdog
+1054,underrated
+1055,understated
+1056,underwater
+1057,unfunny
+1058,unintentionally funny
+1059,unique
+1060,united nations
+1061,unlikeable characters
+1062,unlikely friendships
+1063,unrealistic
+1064,unusual plot structure
+1065,us history
+1066,utopia
+1067,vampire
+1068,vampire human love
+1069,vampires
+1070,vengeance
+1071,very funny
+1072,very good
+1073,very interesting
+1074,video game
+1075,video game adaptation
+1076,video games
+1077,videogame
+1078,vienna
+1079,vietnam
+1080,vietnam war
+1081,view askew
+1082,vigilante
+1083,vigilantism
+1084,violence
+1085,violent
+1086,virginity
+1087,virtual reality
+1088,virus
+1089,visceral
+1090,visual
+1091,visually appealing
+1092,visually stunning
+1093,visuals
+1094,voodoo
+1095,voyeurism
+1096,war
+1097,war movie
+1098,wartime
+1099,waste of time
+1100,watch the credits
+1101,weapons
+1102,wedding
+1103,weed
+1104,weird
+1105,werewolf
+1106,werewolves
+1107,western
+1108,whimsical
+1109,wilderness
+1110,wine
+1111,wistful
+1112,witch
+1113,witches
+1114,witty
+1115,wizards
+1116,women
+1117,working class
+1118,workplace
+1119,world politics
+1120,world war i
+1121,world war ii
+1122,writer's life
+1123,writers
+1124,writing
+1125,wuxia
+1126,wwii
+1127,zombie
+1128,zombies

movielens_32m/ml-32m/README.txt

@@ -0,0 +1,180 @@
+Summary
+=======
+
+This dataset (ml-32m) describes 5-star rating and free-text tagging activity from [MovieLens](http://movielens.org), a movie recommendation service. It contains 32000204 ratings and 2000072 tag applications across 87585 movies. These data were created by 200948 users between January 09, 1995 and October 12, 2023. This dataset was generated on October 13, 2023.
+
+Users were selected at random for inclusion. All selected users had rated at least 20 movies. No demographic information is included. Each user is represented by an id, and no other information is provided.
+
+The data are contained in the files `links.csv`, `movies.csv`, `ratings.csv` and `tags.csv`. More details about the contents and use of all these files follows.
+
+This and other GroupLens data sets are publicly available for download at <http://grouplens.org/datasets/>.
+
+
+Usage License
+=============
+
+Neither the University of Minnesota nor any of the researchers involved can guarantee the correctness of the data, its suitability for any particular purpose, or the validity of results based on the use of the data set. The data set may be used for any research purposes under the following conditions:
+
+* The user may not state or imply any endorsement from the University of Minnesota or the GroupLens Research Group.
+* The user must acknowledge the use of the data set in publications resulting from the use of the data set (see below for citation information).
+* The user may redistribute the data set, including transformations, so long as it is distributed under these same license conditions.
+* The user may not use this information for any commercial or revenue-bearing purposes without first obtaining permission from a faculty member of the GroupLens Research Project at the University of Minnesota.
+* The executable software scripts are provided "as is" without warranty of any kind, either expressed or implied, including, but not limited to, the implied warranties of merchantability and fitness for a particular purpose. The entire risk as to the quality and performance of them is with you. Should the program prove defective, you assume the cost of all necessary servicing, repair or correction.
+
+In no event shall the University of Minnesota, its affiliates or employees be liable to you for any damages arising out of the use or inability to use these programs (including but not limited to loss of data or data being rendered inaccurate).
+
+If you have any further questions or comments, please email <grouplens-info@umn.edu>
+
+
+Citation
+========
+
+To acknowledge use of the dataset in publications, please cite the following paper:
+
+> F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens Datasets: History and Context. ACM Transactions on Interactive Intelligent Systems (TiiS) 5, 4: 19:1–19:19. <https://doi.org/10.1145/2827872>
+
+
+Further Information About GroupLens
+===================================
+
+GroupLens is a research group in the Department of Computer Science and Engineering at the University of Minnesota. Since its inception in 1992, GroupLens's research projects have explored a variety of fields including:
+
+* recommender systems
+* online communities
+* mobile and ubiquitious technologies
+* digital libraries
+* local geographic information systems
+
+GroupLens Research operates a movie recommender based on collaborative filtering, MovieLens, which is the source of these data. We encourage you to visit <http://movielens.org> to try it out! If you have exciting ideas for experimental work to conduct on MovieLens, send us an email at <grouplens-info@cs.umn.edu> - we are always interested in working with external collaborators.
+
+
+Content and Use of Files
+========================
+
+Verifying the Dataset Contents
+------------------------------
+
+The following files (with the provided [MD5 checksums](http://en.wikipedia.org/wiki/Md5sum)) should be present in this zip file:
+
+
+| MD5 | File |
+| --- | --- |
+| 8f033867bcb4e6be8792b21468b4fa6e | links.csv |
+| 0df90835c19151f9d819d0822e190797 | movies.csv |
+| cf12b74f9ad4b94a011f079e26d4270a | ratings.csv |
+| 963bf4fa4de6b8901868fddd3eb54567 | tags.csv |
+
+
+We encourage you to verify that the dataset you have on your computer is identical to the ones hosted at [grouplens.org](http://grouplens.org).  This is an important step if you downloaded the dataset from a location other than [grouplens.org](http://grouplens.org), or if you wish to publish research results based on analysis of the MovieLens dataset.
+
+To verify the dataset (after unzipping):
+
+    # on linux
+    md5sum *; cat checksums.txt
+
+    # on OSX
+    md5 *; cat checksums.txt
+
+    # windows users can download a tool from Microsoft (or elsewhere) that verifies MD5 checksums
+
+Check that the two lines of output contain the same hash value.
+
+
+Formatting and Encoding
+-----------------------
+
+The dataset files are written as [comma-separated values](http://en.wikipedia.org/wiki/Comma-separated_values) files with a single header row. Columns that contain commas (`,`) are escaped using double-quotes (`"`). These files are encoded as UTF-8. If accented characters in movie titles or tag values (e.g. Misérables, Les (1995)) display incorrectly, make sure that any program reading the data, such as a text editor, terminal, or script, is configured for UTF-8.
+
+
+User Ids
+--------
+
+MovieLens users were selected at random for inclusion. Their ids have been anonymized. User ids are consistent between `ratings.csv` and `tags.csv` (i.e., the same id refers to the same user across the two files).
+
+
+Movie Ids
+---------
+
+Only movies with at least one rating or tag are included in the dataset. These movie ids are consistent with those used on the MovieLens web site (e.g., id `1` corresponds to the URL <https://movielens.org/movies/1>). Movie ids are consistent between `ratings.csv`, `tags.csv`, `movies.csv`, and `links.csv` (i.e., the same id refers to the same movie across these four data files).
+
+
+Ratings Data File Structure (ratings.csv)
+-----------------------------------------
+
+All ratings are contained in the file `ratings.csv`. Each line of this file after the header row represents one rating of one movie by one user, and has the following format:
+
+    userId,movieId,rating,timestamp
+
+The lines within this file are ordered first by userId, then, within user, by movieId.
+
+Ratings are made on a 5-star scale, with half-star increments (0.5 stars - 5.0 stars).
+
+Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
+
+
+Tags Data File Structure (tags.csv)
+-----------------------------------
+
+All tags are contained in the file `tags.csv`. Each line of this file after the header row represents one tag applied to one movie by one user, and has the following format:
+
+    userId,movieId,tag,timestamp
+
+The lines within this file are ordered first by userId, then, within user, by movieId.
+
+Tags are user-generated metadata about movies. Each tag is typically a single word or short phrase. The meaning, value, and purpose of a particular tag is determined by each user.
+
+Timestamps represent seconds since midnight Coordinated Universal Time (UTC) of January 1, 1970.
+
+
+Movies Data File Structure (movies.csv)
+---------------------------------------
+
+Movie information is contained in the file `movies.csv`. Each line of this file after the header row represents one movie, and has the following format:
+
+    movieId,title,genres
+
+Movie titles are entered manually or imported from <https://www.themoviedb.org/>, and include the year of release in parentheses. Errors and inconsistencies may exist in these titles.
+
+Genres are a pipe-separated list, and are selected from the following:
+
+* Action
+* Adventure
+* Animation
+* Children's
+* Comedy
+* Crime
+* Documentary
+* Drama
+* Fantasy
+* Film-Noir
+* Horror
+* Musical
+* Mystery
+* Romance
+* Sci-Fi
+* Thriller
+* War
+* Western
+* (no genres listed)
+
+
+Links Data File Structure (links.csv)
+---------------------------------------
+
+Identifiers that can be used to link to other sources of movie data are contained in the file `links.csv`. Each line of this file after the header row represents one movie, and has the following format:
+
+    movieId,imdbId,tmdbId
+
+movieId is an identifier for movies used by <https://movielens.org>. E.g., the movie Toy Story has the link <https://movielens.org/movies/1>.
+
+imdbId is an identifier for movies used by <http://www.imdb.com>. E.g., the movie Toy Story has the link <http://www.imdb.com/title/tt0114709/>.
+
+tmdbId is an identifier for movies used by <https://www.themoviedb.org>. E.g., the movie Toy Story has the link <https://www.themoviedb.org/movie/862>.
+
+Use of the resources listed above is subject to the terms of each provider.
+
+
+Cross-Validation
+----------------
+
+Prior versions of the MovieLens dataset included either pre-computed cross-folds or scripts to perform this computation. We no longer bundle either of these features with the dataset, since most modern toolkits provide this as a built-in feature. If you wish to learn about standard approaches to cross-fold computation in the context of recommender systems evaluation, see [LensKit](http://lenskit.org) for tools, documentation, and open-source code examples.

movielens_32m/ml-32m/checksums.txt

@@ -0,0 +1,4 @@
+8f033867bcb4e6be8792b21468b4fa6e  links.csv
+0df90835c19151f9d819d0822e190797  movies.csv
+cf12b74f9ad4b94a011f079e26d4270a  ratings.csv
+963bf4fa4de6b8901868fddd3eb54567  tags.csv

movielens_32m/ml-32m/ratings.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:91159850e41ee59c86231165a688709647e2726cab2e7ba9faf04001bd5261ee
+size 877076222

netflix/README

@@ -0,0 +1,156 @@
+SUMMARY
+================================================================================
+
+This dataset was constructed to support participants in the Netflix Prize.  See
+http://www.netflixprize.com for details about the prize.
+
+The movie rating files contain over 100 million ratings from 480 thousand
+randomly-chosen, anonymous Netflix customers over 17 thousand movie titles.  The
+data were collected between October, 1998 and December, 2005 and reflect the
+distribution of all ratings received during this period.  The ratings are on a
+scale from 1 to 5 (integral) stars. To protect customer privacy, each customer
+id has been replaced with a randomly-assigned id.  The date of each rating and
+the title and year of release for each movie id are also provided.
+
+
+USAGE LICENSE
+================================================================================
+
+Netflix can not guarantee the correctness of the data, its suitability for any
+particular purpose, or the validity of results based on the use of the data set.
+The data set may be used for any research purposes under the following
+conditions:
+
+     * The user may not state or imply any endorsement from Netflix.
+
+     * The user must acknowledge the use of the data set in
+       publications resulting from the use of the data set, and must
+       send us an electronic or paper copy of those publications.
+
+     * The user may not redistribute the data without separate
+       permission.
+
+     * The user may not use this information for any commercial or
+       revenue-bearing purposes without first obtaining permission
+       from Netflix.
+
+If you have any further questions or comments, please contact the Prize
+administrator <prizemaster@netflix.com>
+
+
+TRAINING DATASET FILE DESCRIPTION
+================================================================================
+
+The file "training_set.tar" is a tar of a directory containing 17770 files, one
+per movie.  The first line of each file contains the movie id followed by a
+colon.  Each subsequent line in the file corresponds to a rating from a customer
+and its date in the following format:
+
+CustomerID,Rating,Date
+
+- MovieIDs range from 1 to 17770 sequentially.
+- CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users.
+- Ratings are on a five star (integral) scale from 1 to 5.
+- Dates have the format YYYY-MM-DD.
+
+MOVIES FILE DESCRIPTION
+================================================================================
+
+Movie information in "movie_titles.txt" is in the following format:
+
+MovieID,YearOfRelease,Title
+
+- MovieID do not correspond to actual Netflix movie ids or IMDB movie ids.
+- YearOfRelease can range from 1890 to 2005 and may correspond to the release of
+  corresponding DVD, not necessarily its theaterical release.
+- Title is the Netflix movie title and may not correspond to 
+  titles used on other sites.  Titles are in English.
+
+
+QUALIFYING AND PREDICTION DATASET FILE DESCRIPTION
+================================================================================
+
+The qualifying dataset for the Netflix Prize is contained in the text file
+"qualifying.txt".  It consists of lines indicating a movie id, followed by a
+colon, and then customer ids and rating dates, one per line for that movie id.
+The movie and customer ids are contained in the training set.  Of course the
+ratings are withheld. There are no empty lines in the file.
+
+MovieID1:
+CustomerID11,Date11
+CustomerID12,Date12
+...
+MovieID2:
+CustomerID21,Date21
+CustomerID22,Date22
+
+For the Netflix Prize, your program must predict the all ratings the customers
+gave the movies in the qualifying dataset based on the information in the
+training dataset.
+
+The format of your submitted prediction file follows the movie and customer id,
+date order of the qualifying dataset.  However, your predicted rating takes the
+place of the corresponding customer id (and date), one per line.
+
+For example, if the qualifying dataset looked like:
+
+111:
+3245,2005-12-19
+5666,2005-12-23
+6789,2005-03-14
+225:
+1234,2005-05-26
+3456,2005-11-07
+
+then a prediction file should look something like:
+111:
+3.0
+3.4
+4.0
+225:
+1.0
+2.0
+
+which predicts that customer 3245 would have rated movie 111 3.0 stars on the
+19th of Decemeber, 2005, that customer 5666 would have rated it slightly higher
+at 3.4 stars on the 23rd of Decemeber, 2005, etc.
+
+You must make predictions for all customers for all movies in the qualifying
+dataset.
+
+THE PROBE DATASET FILE DESCRIPTION
+================================================================================
+
+To allow you to test your system before you submit a prediction set based on the
+qualifying dataset, we have provided a probe dataset in the file "probe.txt".
+This text file contains lines indicating a movie id, followed by a colon, and
+then customer ids, one per line for that movie id.
+
+MovieID1:
+CustomerID11
+CustomerID12
+...
+MovieID2:
+CustomerID21
+CustomerID22
+
+Like the qualifying dataset, the movie and customer id pairs are contained in
+the training set.  However, unlike the qualifying dataset, the ratings (and
+dates) for each pair are contained in the training dataset.
+
+If you wish, you may calculate the RMSE of your predictions against those
+ratings and compare your RMSE against the Cinematch RMSE on the same data.  See
+http://www.netflixprize.com/faq#probe for that value.
+
+
+Good luck!
+
+
+MD5 SIGNATURES AND FILE SIZES
+================================================================================
+
+d2b86d3d9ba8b491d62a85c9cf6aea39        577547 movie_titles.txt
+ed843ae92adbc70db64edbf825024514      10782692 probe.txt
+88be8340ad7b3c31dfd7b6f87e7b9022      52452386 qualifying.txt
+0e13d39f97b93e2534104afc3408c68c           567 rmse.pl
+0098ee8997ffda361a59bc0dd1bdad8b    2081556480 training_set.tar

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retailrocket/events.csv

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+size 94237913

retailrocket/item_properties_part2.csv

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+size 408929907

yoochoose/dataset-README.txt

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+SUMMARY
+================================================================================
+
+This dataset was constructed by YOOCHOOSE GmbH to support participants in the RecSys Challenge 2015.
+See  http://recsys.yoochoose.net for details about the challenge.
+
+The YOOCHOOSE dataset contain a collection of sessions from a retailer, where each session
+is encapsulating the click events that the user performed in the session.
+For some of the sessions, there are also buy events; means that the session ended
+with the user bought something from the web shop. The data was collected during several
+months in the year of 2014, reflecting the clicks and purchases performed by the users
+of an on-line retailer in Europe.  To protect end users privacy, as well as the retailer,
+all numbers have been modified. Do not try to reveal the identity of the retailer.
+
+LICENSE
+================================================================================
+This dataset is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
+International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
+YOOCHOOSE cannot guarantee the completeness and correctness of the data or the validity
+of results based on the use of the dataset as it was collected by implicit tracking of a website. 
+If you have any further questions or comments, please contact YooChoose <support@YooChoose.com>. 
+The data is provided "as it is" and there is no obligation of YOOCHOOSE to correct it,
+improve it or to provide additional information about it.
+
+CLICKS DATASET FILE DESCRIPTION
+================================================================================
+The file yoochoose-clicks.dat comprising the clicks of the users over the items.
+Each record/line in the file has the following fields/format: Session ID, Timestamp, Item ID, Category
+-Session ID – the id of the session. In one session there are one or many clicks. Could be represented as an integer number.
+-Timestamp – the time when the click occurred. Format of YYYY-MM-DDThh:mm:ss.SSSZ
+-Item ID – the unique identifier of the item that has been clicked. Could be represented as an integer number.
+-Category – the context of the click. The value "S" indicates a special offer, "0" indicates  a missing value, a number between 1 to 12 indicates a real category identifier,
+ any other number indicates a brand. E.g. if an item has been clicked in the context of a promotion or special offer then the value will be "S", if the context was a brand i.e BOSCH,
+ then the value will be an 8-10 digits number. If the item has been clicked under regular category, i.e. sport, then the value will be a number between 1 to 12. 
+ 
+BUYS DATSET FILE DESCRIPTION
+================================================================================
+The file yoochoose-buys.dat comprising the buy events of the users over the items.
+Each record/line in the file has the following fields: Session ID, Timestamp, Item ID, Price, Quantity
+
+-Session ID - the id of the session. In one session there are one or many buying events. Could be represented as an integer number.
+-Timestamp - the time when the buy occurred. Format of YYYY-MM-DDThh:mm:ss.SSSZ
+-Item ID – the unique identifier of item that has been bought. Could be represented as an integer number.
+-Price – the price of the item. Could be represented as an integer number.
+-Quantity – the quantity in this buying.  Could be represented as an integer number.
+
+TEST DATASET FILE DESCRIPTION
+================================================================================
+The file yoochoose-test.dat comprising only clicks of users over items.
+This file served as a test file in the RecSys challenge 2015. 
+The structure is identical to the file yoochoose-clicks.dat but you will not find the
+corresponding buying events to these sessions in the yoochoose-buys.dat file.

yoochoose/yoochoose-buys.dat

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