| --- |
| language: |
| - en |
| license: apache-2.0 |
| task_categories: |
| - time-series-forecasting |
| tags: |
| - temporal-point-process |
| - event-sequences |
| - amazon-reviews |
| - marked-temporal-point-process |
| size_categories: |
| - n<1K |
| --- |
| |
| # Amazon Product Review Events |
|
|
| Curated per-user product review sequences from [Amazon Reviews 2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) (McAuley Lab), designed for temporal point process (TPP) and marked temporal point process (MTPP) modeling. Each sequence captures a single user's chronologically-ordered reviews within one product category. |
|
|
| Sequences are aggressively filtered to prevent pattern exploitation — uninformative "other" events are removed, and sequences with dominant-type or consecutive-repeat shortcuts are dropped to ensure models must perform genuine reasoning. |
|
|
| ## Dataset Description |
|
|
| - **Source:** [Amazon Reviews 2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) (McAuley Lab, HuggingFace) |
| - **Categories:** Electronics, Books, Home & Kitchen, Beauty & Personal Care |
| - **Grouping:** Events grouped by user within a single product category |
| - **Sequences:** 229 |
| - **Sequence length:** 50–78 events per sequence (mean: 58.1) |
| - **Event types:** 40 sub-categories (no "other") |
| - **Time unit:** weeks |
|
|
| ## Schema |
|
|
| Each record is a dictionary with 10 fields: |
|
|
| | Field | Type | Description | |
| |---|---|---| |
| | `seq_idx` | int | Sequence index | |
| | `seq_len` | int | Number of events in the sequence | |
| | `type_category` | str | Parent product category (e.g., `"Electronics"`) | |
| | `span_weeks` | float | Total time span of the sequence (weeks) | |
| | `description` | str | Category, user activity window, and review period | |
| | `metadata` | str (JSON) | `user_id`, `parent_category`, `num_reviews`, `num_sub_categories`, `date_range_start`, `date_range_end`, `span_weeks`, `time_unit` | |
| | `time_since_start` | list[float] | Time since the first event (in weeks) | |
| | `time_since_last_event` | list[float] | Time since the previous event (in weeks) | |
| | `type_event` | list[str] | Product sub-category slug (see below) | |
| | `type_text` | list[str] | Natural language description with first sub-category name, product title, star rating, and review title. HTML tags, entities, embedded media tags, and URLs are stripped. | |
|
|
| ## Event Types (40 sub-categories) |
|
|
| Event types are the 2nd-level product sub-categories, normalized to lowercase slugs. Rare sub-categories are mapped to `"other"` during initial curation, and all `"other"` events are then stripped from the final sequences. Products without a proper category hierarchy (e.g., generic "All Electronics") are excluded. |
|
|
| **Home & Kitchen** (159 sequences, 10 types): `kitchen_dining` (2,131), `home_d_cor_products` (1,888), `bedding` (1,520), `furniture` (1,018), `storage_organization` (837), `bath` (814), `heating_cooling_air_quality` (272), `wall_art` (254), `seasonal_d_cor` (228), `event_party_supplies` (170) |
|
|
| **Beauty & Personal Care** (57 sequences, 10 types): `hair_care` (696), `skin_care` (693), `makeup` (642), `tools_accessories` (483), `foot_hand_nail_care` (437), `personal_care` (163), `shave_hair_removal` (127), `fragrance` (108), `salon_spa` (80), `men_s_grooming` (3) |
|
|
| **Electronics** (10 sequences, 10 types): `computers_accessories` (119), `television_video` (98), `home_audio` (84), `camera_photo` (71), `portable_audio_video` (58), `accessories_supplies` (39), `security_surveillance` (38), `headphones_earbuds_accessories` (37), `car_vehicle_electronics` (27), `gps_finders_accessories` (5) |
|
|
| **Books** (3 sequences, 10 types): `literature_fiction` (40), `history` (25), `biographies_memoirs` (20), `children_s_books` (17), `arts_photography` (17), `teen_young_adult` (15), `mystery_thriller_suspense` (11), `christian_books_bibles` (7), `politics_social_sciences` (5), `crafts_hobbies_home` (2) |
|
|
| ## Curation Filters |
|
|
| Sequences are selected to represent moderately prolific reviewers with diverse, non-trivial sub-category distributions: |
|
|
| | Filter | Value | Description | |
| |---|---|---| |
| | `min-events` | 50 | Min reviews per user sequence | |
| | `max-events` | 80 | Max reviews per user sequence | |
| | `min-types` | 3 | At least 3 distinct sub-category types | |
| | `min-span` | 3 months | Exclude sequences spanning less than 3 months | |
| | `max-span` | 60 months | Exclude sequences spanning 60+ months | |
| | `max-sub-categories` | 10 | Top 10 sub-categories kept per parent category; rest mapped to `"other"` | |
| | `min-subcat-count` | 50 | Sub-categories with fewer than 50 products mapped to `"other"` | |
| | Verified purchase only | — | Only verified purchase reviews are included | |
| | Exclude catch-all categories | — | Products without proper sub-category hierarchy (e.g., "All Electronics") are excluded | |
| | Remove "other" events | — | All `"other"` events stripped; `time_since_last_event` recomputed | |
| | `max-dominant-ratio` | 0.30 | Drop sequences where any single type exceeds 30% of events | |
| | `max-repeat-ratio` | 0.35 | Drop sequences where consecutive same-type rate exceeds 35% | |
|
|
| ## Event Text |
|
|
| Each event's `type_text` is a natural language sentence containing the first sub-category, product title, star rating, and review title: |
|
|
| > The user reviewed "Instant Pot Duo 7-in-1 Electric Pressure Cooker" under Kitchen & Dining, rating it 5 out of 5 stars. Their review says: "Best kitchen purchase ever". |
|
|
| The model must learn to predict the product sub-category from product titles, sub-category context, rating patterns, and review titles. |
|
|
| ## Example |
|
|
| ```json |
| { |
| "seq_idx": 0, |
| "seq_len": 61, |
| "type_category": "Electronics", |
| "span_weeks": 269.34, |
| "description": "Amazon Electronics review timeline for a user spanning May 2015 to Jul 2020. This sequence tracks the user's product review activity within the Electronics category on Amazon.", |
| "metadata": "{\"user_id\": \"AF7S6K2L...\", \"parent_category\": \"Electronics\", \"num_reviews\": 76, \"num_sub_categories\": 10, ...}", |
| "time_since_start": [0.0, 0.0016, 0.0024, ...], |
| "time_since_last_event": [0.0, 0.0016, 0.0008, ...], |
| "type_event": ["camera_photo", "computers_accessories", "accessories_supplies", ...], |
| "type_text": ["The user reviewed \"GE 26571 Line Cord with Coupler (25 Feet, White)\" under Accessories & Supplies, rating it 3 out of 5 stars. Their review says: \"Like quality\".", ...] |
| } |
| ``` |
|
|
| ## Intended Use |
|
|
| - Training and evaluating temporal point process models |
| - Studying consumer purchasing patterns across product sub-categories |
| - Benchmarking next-event prediction and event forecasting |
| - Modeling marked temporal point processes with rich text marks |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite: |
|
|
| ```bibtex |
| @dataset{amazon_review_events_2025, |
| title={Amazon Product Review Events}, |
| author={XiaoBB}, |
| year={2025}, |
| url={https://huggingface.co/datasets/XiaoBB/amazon_review_events}, |
| note={Curated from Amazon Reviews 2023 (McAuley Lab)} |
| } |
| ``` |
|
|
