Datasets:
Tasks:
Other
Formats:
json
Languages:
English
Size:
10M - 100M
ArXiv:
Tags:
recommendation
sequential-recommendation
generative-recommendation
amazon-reviews
recsys
leave-one-out
License:
| license: other | |
| license_name: amazon-reviews-2023 | |
| license_link: https://amazon-reviews-2023.github.io/ | |
| language: | |
| - en | |
| pretty_name: 'Amazon Reviews 2023 — User Interactions (5-core, leave-one-out, sequential)' | |
| size_categories: | |
| - 10M<n<100M | |
| tags: | |
| - recommendation | |
| - sequential-recommendation | |
| - generative-recommendation | |
| - amazon-reviews | |
| - recsys | |
| - leave-one-out | |
| - 5-core | |
| task_categories: | |
| - other | |
| configs: | |
| - config_name: seq_maxlen50_Video_Games | |
| data_files: | |
| - split: train | |
| path: seq_maxlen50/Video_Games.train.jsonl | |
| - split: validation | |
| path: seq_maxlen50/Video_Games.valid.jsonl | |
| - split: test | |
| path: seq_maxlen50/Video_Games.test.jsonl | |
| - config_name: seq_maxlen50_Industrial_and_Scientific | |
| data_files: | |
| - split: train | |
| path: seq_maxlen50/Industrial_and_Scientific.train.jsonl | |
| - split: validation | |
| path: seq_maxlen50/Industrial_and_Scientific.valid.jsonl | |
| - split: test | |
| path: seq_maxlen50/Industrial_and_Scientific.test.jsonl | |
| - config_name: seq_maxlen50_Beauty_and_Personal_Care | |
| data_files: | |
| - split: train | |
| path: seq_maxlen50/Beauty_and_Personal_Care.train.jsonl | |
| - split: validation | |
| path: seq_maxlen50/Beauty_and_Personal_Care.valid.jsonl | |
| - split: test | |
| path: seq_maxlen50/Beauty_and_Personal_Care.test.jsonl | |
| - config_name: seq_maxlen50_Musical_Instruments | |
| data_files: | |
| - split: train | |
| path: seq_maxlen50/Musical_Instruments.train.jsonl | |
| - split: validation | |
| path: seq_maxlen50/Musical_Instruments.valid.jsonl | |
| - split: test | |
| path: seq_maxlen50/Musical_Instruments.test.jsonl | |
| - config_name: seq_maxlen50_Books | |
| data_files: | |
| - split: train | |
| path: seq_maxlen50/Books.train.jsonl | |
| - split: validation | |
| path: seq_maxlen50/Books.valid.jsonl | |
| - split: test | |
| path: seq_maxlen50/Books.test.jsonl | |
| - config_name: seq_maxlen20_Video_Games | |
| data_files: | |
| - split: train | |
| path: seq_maxlen20/Video_Games.train.jsonl | |
| - split: validation | |
| path: seq_maxlen20/Video_Games.valid.jsonl | |
| - split: test | |
| path: seq_maxlen20/Video_Games.test.jsonl | |
| - config_name: seq_maxlen20_Industrial_and_Scientific | |
| data_files: | |
| - split: train | |
| path: seq_maxlen20/Industrial_and_Scientific.train.jsonl | |
| - split: validation | |
| path: seq_maxlen20/Industrial_and_Scientific.valid.jsonl | |
| - split: test | |
| path: seq_maxlen20/Industrial_and_Scientific.test.jsonl | |
| - config_name: seq_maxlen20_Beauty_and_Personal_Care | |
| data_files: | |
| - split: train | |
| path: seq_maxlen20/Beauty_and_Personal_Care.train.jsonl | |
| - split: validation | |
| path: seq_maxlen20/Beauty_and_Personal_Care.valid.jsonl | |
| - split: test | |
| path: seq_maxlen20/Beauty_and_Personal_Care.test.jsonl | |
| - config_name: seq_maxlen20_Musical_Instruments | |
| data_files: | |
| - split: train | |
| path: seq_maxlen20/Musical_Instruments.train.jsonl | |
| - split: validation | |
| path: seq_maxlen20/Musical_Instruments.valid.jsonl | |
| - split: test | |
| path: seq_maxlen20/Musical_Instruments.test.jsonl | |
| - config_name: seq_maxlen20_Books | |
| data_files: | |
| - split: train | |
| path: seq_maxlen20/Books.train.jsonl | |
| - split: validation | |
| path: seq_maxlen20/Books.valid.jsonl | |
| - split: test | |
| path: seq_maxlen20/Books.test.jsonl | |
| - config_name: last_out_Video_Games | |
| data_files: | |
| - split: train | |
| path: last_out/Video_Games.train.jsonl | |
| - split: validation | |
| path: last_out/Video_Games.valid.jsonl | |
| - split: test | |
| path: last_out/Video_Games.test.jsonl | |
| - config_name: last_out_Industrial_and_Scientific | |
| data_files: | |
| - split: train | |
| path: last_out/Industrial_and_Scientific.train.jsonl | |
| - split: validation | |
| path: last_out/Industrial_and_Scientific.valid.jsonl | |
| - split: test | |
| path: last_out/Industrial_and_Scientific.test.jsonl | |
| - config_name: last_out_Beauty_and_Personal_Care | |
| data_files: | |
| - split: train | |
| path: last_out/Beauty_and_Personal_Care.train.jsonl | |
| - split: validation | |
| path: last_out/Beauty_and_Personal_Care.valid.jsonl | |
| - split: test | |
| path: last_out/Beauty_and_Personal_Care.test.jsonl | |
| - config_name: last_out_Musical_Instruments | |
| data_files: | |
| - split: train | |
| path: last_out/Musical_Instruments.train.jsonl | |
| - split: validation | |
| path: last_out/Musical_Instruments.valid.jsonl | |
| - split: test | |
| path: last_out/Musical_Instruments.test.jsonl | |
| - config_name: last_out_Books | |
| data_files: | |
| - split: train | |
| path: last_out/Books.train.jsonl | |
| - split: validation | |
| path: last_out/Books.valid.jsonl | |
| - split: test | |
| path: last_out/Books.test.jsonl | |
| - config_name: interactions_Video_Games | |
| data_files: | |
| - split: train | |
| path: interactions/Video_Games.jsonl | |
| - config_name: interactions_Industrial_and_Scientific | |
| data_files: | |
| - split: train | |
| path: interactions/Industrial_and_Scientific.jsonl | |
| - config_name: interactions_Beauty_and_Personal_Care | |
| data_files: | |
| - split: train | |
| path: interactions/Beauty_and_Personal_Care.jsonl | |
| - config_name: interactions_Musical_Instruments | |
| data_files: | |
| - split: train | |
| path: interactions/Musical_Instruments.jsonl | |
| - config_name: interactions_Books | |
| data_files: | |
| - split: train | |
| path: interactions/Books.jsonl | |
| # Amazon Reviews 2023 — User Interactions (5-core, leave-one-out, sequential) | |
| User–item **interaction** data for five Amazon Reviews 2023 categories, processed | |
| into ready-to-use **sequential / generative recommendation** splits with the | |
| *de-facto standard* recipe (**5-core filtering → chronological ordering → | |
| leave-one-out split**). | |
| Every record keeps the **timestamp**, and the splits are **byte-for-byte | |
| reproducible** from the official Amazon Reviews 2023 release; the statistics also | |
| **match, exactly, numbers reported by multiple peer-reviewed papers** | |
| (see [§ Validation](#validation--double-check)). | |
| > **Item content features** (title, images, price, brand, …) are in the companion | |
| > dataset **[`yufan/amazon2023-item-metadata`](https://huggingface.co/datasets/yufan/amazon2023-item-metadata)**, joined by | |
| > `parent_asin`. | |
| All files are **JSON Lines** (`.jsonl`). | |
| ## Subsets (configs) & splits | |
| Configs are named **`<processing>_<Category>`** and grouped by processing method; | |
| each has `train` / `validation` / `test` splits (single `train` for | |
| `interactions_*`). `5 categories × 4 processing variants = 20 configs`. | |
| | Processing variant (config prefix) | What it is | | |
| |---|---| | |
| | `seq_maxlen50_*` | leave-one-out **sequential** split, history ≤ 50 (ID-based / SASRec-style) | | |
| | `seq_maxlen20_*` | leave-one-out **sequential** split, history ≤ 20 (semantic-ID / generative) | | |
| | `last_out_*` | leave-one-out **direct** split (no history column; MF/BPR-style) | | |
| | `interactions_*` | 5-core interaction table (no split applied; single `train`) | | |
| Categories: `Video_Games`, `Industrial_and_Scientific`, | |
| `Beauty_and_Personal_Care`, `Musical_Instruments`, `Books`. | |
| ```python | |
| from datasets import load_dataset | |
| # sequential split (history <= 50), Video Games | |
| ds = load_dataset("yufan/amazon2023-user-interactions", "seq_maxlen50_Video_Games") | |
| train, valid, test = ds["train"], ds["validation"], ds["test"] | |
| # item content features (companion dataset), same category | |
| meta = load_dataset("yufan/amazon2023-item-metadata", "Video_Games")["train"] | |
| ``` | |
| ### Which config do I use? | |
| | If your model is … | Use | | |
| |---|---| | |
| | Sequential / ID-based (SASRec, GRU4Rec) | `seq_maxlen50_<Category>` | | |
| | Generative / Semantic-ID (TIGER) | `seq_maxlen20_<Category>` | | |
| | Non-sequential (MF, BPR, LightGCN) | `last_out_<Category>` | | |
| | Custom (re-split yourself) | `interactions_<Category>` | | |
| ### Repository layout | |
| ``` | |
| interactions/<Category>.jsonl | |
| last_out/<Category>.{train,valid,test}.jsonl | |
| seq_maxlen20/<Category>.{train,valid,test}.jsonl | |
| seq_maxlen50/<Category>.{train,valid,test}.jsonl | |
| stats.json stats.md | |
| ``` | |
| ### Record schemas | |
| **`interactions_*` and `last_out_*`** — one interaction per line: | |
| ```json | |
| {"user_id": "AEVPP…N7WFQ", "parent_asin": "B09JY72CNG", "rating": 4.0, "timestamp": 1630594913298} | |
| ``` | |
| **`seq_maxlen20_*` / `seq_maxlen50_*`** — same, plus the chronological history | |
| (list of `parent_asin`, most-recent-last, capped at the max length): | |
| ```json | |
| {"user_id": "AEVPP…N7WFQ", "parent_asin": "B09JY72CNG", "rating": 4.0, | |
| "timestamp": 1630594913298, "history": ["B08R5B7YS4", "B0863MT183", "…"]} | |
| ``` | |
| `parent_asin` is the Amazon product (parent) id — the standard item id for the | |
| Amazon Reviews 2023 benchmark, and the **join key** to the item-metadata dataset. | |
| --- | |
| ## Data-processing protocol | |
| Source: the official **Amazon Reviews 2023** | |
| ([McAuley-Lab/Amazon-Reviews-2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023), | |
| [website](https://amazon-reviews-2023.github.io/)). | |
| 1. **Input:** official de-duplicated raw interactions (`benchmark/.../rating_only`). | |
| 2. **5-core filtering:** iteratively drop users and items with < 5 interactions | |
| until every remaining user **and** item has ≥ 5. | |
| 3. **Chronological ordering:** per user, sort ascending by `timestamp`; keep the | |
| earliest occurrence of each `(user, item)` pair. | |
| 4. **Leave-one-out split:** per user, **last** → `test`, **second-to-last** → | |
| `validation`, the rest → `train`. | |
| 5. **Sequential history:** attach each target's prior history, truncated to the | |
| most recent **20** or **50** items. | |
| The 5-core and leave-one-out logic is borrowed **verbatim** from the official | |
| benchmark scripts | |
| ([`kcore_filtering.py`, `last_out_split.py`](https://github.com/hyp1231/AmazonReviews2023/tree/main/benchmark_scripts)), | |
| so the splits reproduce the official `5core/last_out` exactly. This recipe was | |
| popularised by **TIGER** (Rajput et al., NeurIPS 2023, | |
| [arXiv:2305.05065](https://arxiv.org/abs/2305.05065)). | |
| --- | |
| ## Validation / double-check | |
| **Level 1 — exact match to the official benchmark.** For every category, our | |
| re-derived 5-core interaction count and number of test users are **identical** to | |
| the official `McAuley-Lab/Amazon-Reviews-2023` `5core/rating_only` and | |
| `5core/last_out` files (asserted in the build script). | |
| **Level 2 — exact match to numbers reported in peer-reviewed papers.** Under the | |
| *same conditions* (Amazon Reviews 2023 · same category · 5-core · leave-one-out), | |
| our `(users / items / interactions)` match **to the digit** the dataset | |
| statistics reported by **~15 independent papers**: | |
| | Category | Ours — users / items / interactions | Reported **identically** by (same 5-core + LOO) | | |
| |---|---|---| | |
| | Musical_Instruments | 57,439 / 24,587 / 511,836 | **9 papers** — UTGRec, MTGRec, LARES, CCFRec, Pctx, LLaDA-Rec, HSTU-BLaIR, Augment-or-Not, MLPs | | |
| | Video_Games | 94,762 / 25,612 / 814,586 | **4** — GrIT, Not-Just-What-But-When, MLPs, HSTU-BLaIR | | |
| | Industrial_and_Scientific | 50,985 / 25,848 / 412,947 | **4** — GrIT, MLPs, Augment-or-Not, Token-Weighted | | |
| | Beauty_and_Personal_Care | 729,576 / 207,649 / 6,624,441 | **2** — AlphaFree, Closing-the-Gap | | |
| | Books | 776,370 / 495,063 / 9,488,297 | **2** — Not-Just-What-But-When, Hi-SAM (≈, rounded) | | |
| ≈ 20 reported data points across ~15 independent papers, all matching exactly | |
| (a few differ by 1 from rounding, e.g. HSTU-BLaIR's 814,585 vs 814,586). | |
| <details> | |
| <summary><b>Cross-validation references (papers reporting identical statistics)</b></summary> | |
| UTGRec ([2504.04405](https://arxiv.org/abs/2504.04405)) · | |
| MTGRec / Pre-training Generative Rec. ([2504.04400](https://arxiv.org/abs/2504.04400)) · | |
| LARES ([2505.16865](https://arxiv.org/abs/2505.16865)) · | |
| CCFRec / Bridging Textual-Collaborative ([2503.12183](https://arxiv.org/abs/2503.12183)) · | |
| Pctx ([2510.21276](https://arxiv.org/abs/2510.21276)) · | |
| LLaDA-Rec ([2511.06254](https://arxiv.org/abs/2511.06254)) · | |
| HSTU-BLaIR ([2504.10545](https://arxiv.org/abs/2504.10545)) · | |
| Augment-or-Not ([2505.23053](https://arxiv.org/abs/2505.23053)) · | |
| MLPs ([2605.12617](https://arxiv.org/abs/2605.12617)) · | |
| GrIT ([2602.19728](https://arxiv.org/abs/2602.19728)) · | |
| Not-Just-What-But-When ([2507.23209](https://arxiv.org/abs/2507.23209)) · | |
| Token-Weighted ([2601.17787](https://arxiv.org/abs/2601.17787)) · | |
| AlphaFree ([2603.02653](https://arxiv.org/abs/2603.02653)) · | |
| Closing-the-Gap ([2508.14910](https://arxiv.org/abs/2508.14910)) · | |
| Hi-SAM ([2602.11799](https://arxiv.org/abs/2602.11799)) | |
| </details> | |
| **Near-matches confirm the same base.** A few papers use the same 5-core + LOO | |
| but report *slightly smaller* counts because they add **one extra filter** — e.g. | |
| ReSID drops items lacking structured side-info (≈2–6 % smaller across *every* | |
| category: VG 94,515; Musical 57,359; Books 775,503), Multimodal-GR drops items | |
| without images (Beauty 724,796), SPARC keeps only `rating ≥ 4` (Books 459,133). | |
| Being only marginally smaller is itself evidence that the underlying 5-core + LOO | |
| base is identical. | |
| **Larger differences only arise when the conditions differ** (excluded by the | |
| precondition above): the *old 2014* Beauty (22,363 users; BSARec, RecCocktail, | |
| Understanding-GR, Beyond-Unimodal), *sub-sampling* (Efficient-Responsible 2,289; | |
| Heterogeneous 10k/domain), or *omitting 5-core / temporal-truncation* (R²ec, | |
| Reinforced-PO) — these are the papers' own design choices, not discrepancies in | |
| this dataset. | |
| --- | |
| ## Statistics (after 5-core) | |
| | Category | #Users | #Items | #Interactions | Avg len | Median | Sparsity | | |
| |----------|-------:|-------:|--------------:|--------:|-------:|---------:| | |
| | `Video_Games` | 94,762 | 25,612 | 814,586 | 8.5961 | 6 | 0.99966437 | | |
| | `Industrial_and_Scientific` | 50,985 | 25,848 | 412,947 | 8.0994 | 6 | 0.99968665 | | |
| | `Beauty_and_Personal_Care` | 729,576 | 207,649 | 6,624,441 | 9.0799 | 7 | 0.99995627 | | |
| | `Musical_Instruments` | 57,439 | 24,587 | 511,836 | 8.9109 | 7 | 0.99963757 | | |
| | `Books` | 776,370 | 495,063 | 9,488,297 | 12.2214 | 7 | 0.99997531 | | |
| ### Leave-one-out split row counts | |
| | Category | direct train | valid | test | seq20 train | seq50 train | | |
| |----------|-------------:|------:|-----:|------------:|------------:| | |
| | `Video_Games` | 625,062 | 94,762 | 94,762 | 530,300 | 530,300 | | |
| | `Industrial_and_Scientific` | 310,977 | 50,985 | 50,985 | 259,992 | 259,992 | | |
| | `Beauty_and_Personal_Care` | 5,165,289 | 729,576 | 729,576 | 4,435,713 | 4,435,713 | | |
| | `Musical_Instruments` | 396,958 | 57,439 | 57,439 | 339,519 | 339,519 | | |
| | `Books` | 7,935,557 | 776,370 | 776,370 | 7,159,187 | 7,159,187 | | |
| (`valid` = `test` = #users by construction. Sequential `train` uses the rolling | |
| next-item scheme; the 20/50 variants share the same row count, differing only in | |
| history length.) | |
| --- | |
| ## Intended uses & limitations | |
| Next-item sequential recommendation, generative / semantic-ID recommendation, | |
| content/multimodal recommendation (with the companion item-metadata dataset), | |
| cold-start studies. Implicit feedback (all retained reviews are positives; use | |
| `rating` for explicit labels). No leakage by construction. The two largest | |
| categories (Books, Beauty) are processed from the official pre-filtered 5-core | |
| data (verified equivalent). | |
| ## License & citation | |
| Derived from **Amazon Reviews 2023** (McAuley Lab); for **research use**. Please | |
| cite: | |
| > Yupeng Hou, Jiacheng Li, Zhankui He, An Yan, Xiusi Chen, Julian McAuley. | |
| > *Bridging Language and Items for Retrieval and Recommendation.* 2024. | |
| > [arXiv:2403.03952](https://arxiv.org/abs/2403.03952) · | |
| > https://amazon-reviews-2023.github.io/ | |
| > Shashank Rajput et al. *Recommender Systems with Generative Retrieval (TIGER).* | |
| > NeurIPS 2023. [arXiv:2305.05065](https://arxiv.org/abs/2305.05065) | |