---
pretty_name: Cross-Domain RecSys Interactions (15 sources, ~883M)
license: other
language: [en, zh, "no"]
tags:
- recommendation
- sequential-recommendation
- cross-domain
- collaborative-filtering
- implicit-feedback
size_categories:
- 100M<n<1B
task_categories:
- other
configs:
- config_name: default
  data_files: data/interactions_v*.parquet
---

# Cross-Domain RecSys Interactions — 15 sources, integer-indexed

One unified, integer-indexed **interaction** corpus glued from **15 recommendation sources** for
**foundation / sequential recommenders**. Built in **3 additive versions** that share one schema and
one global index space, so reading all of them together = the full corpus, collision-free.
**Interactions only — no model, no embeddings.** Item text is provided separately as title+description
metadata, joinable 1:1 by `item_idx`.

**Full corpus: 882,999,680 interactions · ~52.6M users · ~12.1M items.**

## Versions (read all three for the full corpus)

| version | file | interactions | sources | feedback |
|---|---|---:|---|---|
| **v1 — base** (web/review) | `data/interactions_v1.parquet` | 706,928,307 | amazon, goodreads, movielens, yelp, mind, steam | rating / read / click / recommend |
| **v2 — sequential** | `data/interactions_v2.parquet` | 60,093,215 | hm, amazon_m2, foursquare(Gowalla), trivago, lfm(Last.fm-1k), adressa, foodcom | purchase / click / checkin / play / rating |
| **v3 — chinese short-video** | `data/interactions_v3.parquet` | 115,978,158 | pixelrec(Pixel8M), microlens(MicroLens-1M) | comment / click |

Versions are **append-only & index-continuous**: v2 indices start at `max(v1)+1`, v3 at `max(v1∪v2)+1`,
so `user_idx`/`item_idx` ranges are disjoint across versions and a plain union never collides. Each
version was built independently (its own k-core), and earlier versions are never rewritten.

### Per-source breakdown (interactions in train)
amazon 441,756,006 · goodreads 226,413,879 · pixelrec 106,893,985 · movielens 31,904,072 ·
hm 28,384,429 · amazon_m2 16,543,664 · microlens 9,084,173 · yelp 5,718,931 · foursquare 4,369,335 ·
trivago 4,198,784 · lfm 3,218,125 · adressa 2,783,832 · mind 1,104,003 · foodcom 595,046 · steam 31,416.

## Files (uniform `_v{1,2,3}` naming; all parquet, homogeneous per group)

```
data/interactions_v{1,2,3}.parquet   # the corpus (8 cols, identical schema) — glob all 3
maps/user_map_v{1,2,3}.parquet       # user_uid  <-> user_idx
maps/item_map_v{1,2,3}.parquet       # item_uid <-> item_idx <-> (source, native_item)  [meta bridge]
meta/meta_v{1,2,3}.parquet           # item_idx, source, native_item, title, description
valid_items/valid_items_{source}.parquet   # native_item, item_uid, n_inter  (per source, the meta entrypoints)
stats/*.json                         # per-phase build stats
```
v1=base, v2=sequential, v3=chinese (suffix is consistent across interactions, maps and meta).

## Field meanings

**`data/interactions_v*.parquet`** (one row = one user→item interaction)
| field | type | meaning |
|---|---|---|
| `user_idx` | int32 | dense user id, **global** across versions (disjoint ranges), ordered by descending frequency within its version |
| `item_idx` | int32 | dense item id, **global** across versions; the key to join `meta`/`item_map` |
| `timestamp` | int64 | event time, **unix seconds UTC**. Order-only source amazon_m2 uses a synthetic monotonic per-session step |
| `rating` | float32 | rating normalized to **[0,1]** per source (implicit positives = 1.0) |
| `rating_raw` | float32 | original source value (e.g. 5.0 stars, Food.com 0–5; 1.0 for implicit) |
| `feedback_type` | int8 | 0 explicit_rating · 1 implicit_click · 2 implicit_recommend · 3 implicit_read · 4 implicit_purchase · 5 implicit_checkin · 6 implicit_play · 7 implicit_comment |
| `domain` | int16 | sub-domain: amazon category 0–32; other sources 100–115 (one per source/sub) |
| `source` | int8 | 0 amazon · 1 goodreads · 2 yelp · 3 movielens · 4 mind · 5 steam · 6 amazon_m2 · 7 hm · 8 adressa · 9 trivago · 10 foursquare · 11 lfm · 12 foodcom · 13 microlens · 14 pixelrec · 15 douban |

Rows are sorted within each user by `(timestamp, item_idx)` — a stable tie-break so sequence slicing
is reproducible even at daily timestamp granularity. **No train/test split** is applied (do the
temporal cutoff at train time).

**`maps/user_map_v*.parquet`**: `user_uid` (str, namespaced `"{source}:{native_user_id}"`) ↔ `user_idx`.
**`maps/item_map_v*.parquet`**: `item_uid` ↔ `item_idx` ↔ (`source`, `native_item`). `native_item` is the
raw item key in the source (parent_asin / book_id / business_id / movieId / article_id / …) — the join
key for external metadata.
**`meta/meta_v*.parquet`**: `item_idx`, `source`, `native_item`, `title`, `description` (title+description
only). **Languages:** v1/v2 mostly English (amazon_m2 multilingual, adressa Norwegian); **v3 = Chinese**.
**`valid_items/valid_items_{source}.parquet`**: `native_item`, `item_uid`, `n_inter` — the per-source
item lists that survived the k-core; entry points to filter+index any external metadata.

## Metadata coverage
- `meta/meta_v1.parquet` — 10,331,150 items (100% of v1), ~3.3 GB.
- `meta/meta_v2.parquet` — 920,925 items (text-bearing v2 sources: amazon_m2, hm, foodcom, lfm), ~104 MB.
  Textless v2 sources have **no** item title/description upstream and are intentionally absent:
  adressa (url only), trivago (amenity properties only), foursquare/Gowalla (geo only).
- `meta/meta_v3.parquet` — 471,446 items (100% of v3; Chinese), ~39 MB.

## Usage

```python
# Full corpus (all 3 versions) with 🤗 datasets — schemas are identical so they load as one table
from datasets import load_dataset
ds = load_dataset("TOPAPEC/cross-domain-recsys-interactions", split="train", streaming=True)
print(next(iter(ds)))
```

```python
# Local DuckDB: glob all versions + attach titles by item_idx (download large files first)
import duckdb
from huggingface_hub import snapshot_download
d = snapshot_download("TOPAPEC/cross-domain-recsys-interactions", repo_type="dataset",
                      allow_patterns=["data/*", "meta/*", "maps/*"])
con = duckdb.connect()
con.sql(f"""
  SELECT m.source, m.title, count(*) n
  FROM   read_parquet('{d}/data/interactions_v*.parquet') t
  JOIN   read_parquet('{d}/meta/meta_v*.parquet')         m USING (item_idx)
  GROUP  BY 1,2 ORDER BY n DESC LIMIT 10
""").show()
```

## Metadata-join contract (for attaching your own metadata)
Per source: `meta_raw → filter(native_item IN valid_items_{source}) → join(item_map_v* on
(source, native_item)) → keyed by item_idx`. Guarantees (asserted in the build tests): every `item_idx`
in the corpus has exactly one `item_map` row; every `(source, native_item)` resolves to one `item_uid`;
no uid collisions across sources; version index ranges are disjoint.

## Provenance & licenses
Derived ETL (interactions + title/description only) from: Amazon Reviews 2023 (McAuley-Lab), Goodreads
(UCSD), MovieLens 32M (GroupLens), Yelp Open Dataset, MIND (Microsoft), Steam (McAuley), Amazon-M2
(KDD Cup 2023), H&M (Kaggle), Adressa (NTNU), Trivago (RecSys'19), Gowalla (SNAP), Last.fm-1k, Food.com
(Kaggle), MicroLens & PixelRec (Westlake). **You must comply with each source's original license/terms.**
Only anonymized interaction tuples + item title/description are redistributed.