Mozilla/history-search-retrieval

Semantic History (Synthetic)

Semantic History is a synthetic dataset for semantic history search research.
It ships as three normalized Parquet tables:

Split	Description
docs	Search history records with url, title, description, frecency, last_visit_date, and tags.
queries	One row per query, tagged by profile and temporal/multi-label flags.
qrels	Relevance pairs linking queries ↔ docs with rank and relevance.

All content is synthetic (no real browsing logs).

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Temporal Variant

The dataset includes a temporal slice designed to test retrieval with time-aware queries (e.g., “yesterday”, “last week”, “on Black Friday”).
Temporal queries in queries are tagged with variant="temporal" and carry a per-query reference time ref_datetime_iso, which is the anchor used to resolve relative phrases (e.g., “yesterday” → specific date range).

For reproducibility, we also publish the raw temporal profiles (one folder per profile) that were used to construct the Parquet release. Each temporal profile contains:

• history.csv — history rows with last_visit_date (µs epoch) and frecency
• query.csv — temporal queries and expected matches
• temporal_context.json — reference time and locale rules (e.g., timezone, weekend, date format)

See the detailed temporal documentation and per-locale notes here:
https://huggingface.co/datasets/Mozilla/history-search-retrieval/blob/main/raw/profiles/temporal/README.md

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Motivation

• User-oriented IR (per-profile history retrieval)
• Temporal-aware retrieval (e.g., profile histories with a reference time)
• Embedding & ranking evaluation on synthetic history traces

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Data Layout

data/v1/
├── docs.parquet
├── queries.parquet
└── qrels.parquet

Columns

• docs:
  doc_id, url, title, description, frecency, last_visit_date, profile, profile_id, variant

• queries:
  query_id, search_query, profile, profile_id, is_temporal, is_multi, variant, ref_datetime_iso

• qrels:
  query_id, doc_id, profile_id, relevance, rank, variant

profile_id is a stable, hashed identifier per profile folder; variant can be temporal or other configured variants; is_multi indicates multi-label queries.

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Firefox Places Background (schema context)

The dataset docs mimics Firefox’s Places DB:

• moz_places table schema:
  https://searchfox.org/firefox-main/source/toolkit/components/places/nsPlacesTables.h
• Length limits referenced in Places utils:
  title ≤ 4096 chars, description ≤ 256
  https://searchfox.org/firefox-main/source/toolkit/components/places/PlacesUtils.sys.mjs#162
• title from DOM <title>:
  https://searchfox.org/firefox-main/source/dom/svg/SVGTitleElement.cpp
• description from prioritized page metadata:
  https://searchfox.org/firefox-main/source/toolkit/actors/ContentMetaChild.sys.mjs#12

Embedding text: we use title + description.

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How to Load (HF Datasets)

from datasets import load_dataset

dataset_id = "Mozilla/history-search-retrieval"
docs_pd    = load_dataset(dataset_id, name="docs")["train"]
queries_pd = load_dataset(dataset_id, name="queries")["train"]
qrels_pd   = load_dataset(dataset_id, name="qrels")["train"]

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Common Operations

HF Datasets Version

List available profiles

profiles = (docs.to_pandas()[["profile_id","profile","variant"]]
             .drop_duplicates()
             .sort_values(["profile","variant"]))

Filter by profile

pid = "262e49ec20c32c41"
p_docs    = docs.filter(lambda x: x["profile_id"] == pid)
p_queries = queries.filter(lambda x: x["profile_id"] == pid)
p_qrels   = qrels.filter(lambda x: x["profile_id"] == pid)

Temporal / multi-label slices

q_temporal = queries.filter(lambda x: x["variant"] == "temporal")
q_multi    = queries.filter(lambda x: x["is_multi"])

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Pandas Version

import pandas as pd
from datasets import load_dataset

dataset_id = "Mozilla/history-search-retrieval"
docs_pd    = load_dataset(dataset_id, name="docs")["train"].to_pandas()
queries_pd = load_dataset(dataset_id, name="queries")["train"].to_pandas()
qrels_pd   = load_dataset(dataset_id, name="qrels")["train"].to_pandas()

q = queries_pd[["query_id","search_query","profile_id","variant","is_multi"]].set_index("query_id")
r = qrels_pd[["query_id","doc_id","rank","relevance"]].set_index("query_id")
d = docs_pd[["doc_id","url","title"]].set_index("doc_id")

# Reconstruct (query <-> doc/url) pairs
qr = r.join(q, how="inner").reset_index()
query_pairs = (qr.join(d, on="doc_id", how="left")
                 .sort_values(["query_id","rank"])
                 .reset_index(drop=True))

Evaluation

This dataset is intended for retrieval evaluation. Please see the repository for the evaluation scripts/notebooks and metric implementations:

• Precision@k, Recall@k, nDCG@k
• Reciprocal Rank (RR), Average Precision (AP)
• On-Topic Rate@k

Synthetic Data Generation

All profiles, queries, and qrels are synthetic. The pipeline creates per-profile histories and LLM-judged relevance pairs from public English documents.

Overview

1. Source: MS MARCO documents (msmarco-docs.tsv); keep top 500k rows with docid,url,title,body (English).

2. Normalize: build a unified table with url,title,description,topic,lang,domain,combined_text

   • description = first 300 chars of body
   • filter titles to length 5-200
   • combined_text = title + " " + description

3. Sample: draw ~50k examples (English-only for this HF release).

4. Profiles: create 25 synthetic profiles; for each, sample 1k-5k items aligned with profile themes; set random frecency (100-5000) and incremental last_visit_date.

5. Queries & qrels: generate profile-specific queries with an LLM; judge relevance over the profile history; export qrels with rank and relevance=1 and concise query.csv per profile.

Code (full scripts & notebooks):

• https://github.com/mozilla/smart_search/tree/temporal_awareness/preprocessing/generate_profiles
• https://github.com/mozilla/smart_search/blob/temporal_awareness/notebooks/generate_history.ipynb

Additional Dataset

We also include a second dataset built from publicly available synthetic histories:

• Source repo: https://github.com/komosny/synthetic-browsing-history
• Details/paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC11754914/

Countries (English-focused): Australia, Canada, United Kingdom, United States.

Preprocessing

• Deduplicate by URL.
• Fetch title and description with priorities:
  • title: <title> → meta[property=og:title] → meta[name=twitter:title] → <h1>
  • description: meta[name=description] → meta[property=og:description] → meta[name=twitter:description] → summary
• Enforce Firefox-style limits: title ≤ 4096, description ≤ 256.
• Drop records with no title and no description.

Query Construction

• For each profile, randomly sample 50 URLs.
• Use an LLM (gpt-5-mini) to generate 50 semantic search queries that should retrieve the given URL, conditioning on its title and description.

Raw Layer

We maintain a reference raw/ tree for repro, but the canonical interface is the Parquet layer:

raw/profiles/<variant>/<single|multi-label>/<profile>/
  ├── history.csv
  ├── query.csv
  └── temporal_context.json  # only for temporal

Use Parquet for all experiments.
raw/ is reference-only; no remote execution loaders.