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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:
  • 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:
  • 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

13. Foursquare (NYC + Tokyo)

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)