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README.md
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- marked-temporal-point-process
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size_categories:
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- n<1K
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configs:
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- config_name: default
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data_files:
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- split: test
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path: data/test-*
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dataset_info:
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features:
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- name: seq_idx
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dtype: int64
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- name: seq_len
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dtype: int64
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- name: type_category
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dtype: string
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- name: span_weeks
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dtype: float64
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- name: description
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dtype: string
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- name: metadata
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dtype: string
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- name: time_since_start
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list: float64
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- name: time_since_last_event
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list: float64
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- name: type_event
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list: string
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- name: type_text
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list: string
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splits:
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- name: test
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num_bytes: 8869501
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num_examples: 311
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download_size: 4326504
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dataset_size: 8869501
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---
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# Amazon Product Review Events
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- **Source:** [Amazon Reviews 2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) (McAuley Lab, HuggingFace)
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- **Categories:** Electronics, Books, Home & Kitchen, Beauty & Personal Care
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- **Grouping:** Events grouped by user within a single product category
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- **Sequences:**
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- **Sequence length:** 50–78 events per sequence (mean: 58.
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- **Event types:** 40 sub-categories
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- **Time unit:** weeks
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## Schema
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| `time_since_start` | list[float] | Time since the first event (in weeks) |
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| `time_since_last_event` | list[float] | Time since the previous event (in weeks) |
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| `type_event` | list[str] | Product sub-category slug (see below) |
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| `type_text` | list[str] | Natural language description
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## Event Types (40 sub-categories)
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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.
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**Home & Kitchen** (
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**Beauty & Personal Care** (
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**Electronics** (
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**Books** (6 sequences, 10 types): `
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## Curation Filters
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| `max-events` | 80 | Max reviews per user sequence |
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| `min-types` | 3 | At least 3 distinct sub-category types |
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| `min-span` | 6 months | Exclude sequences spanning less than 6 months |
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| `max-span` |
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| `max-sub-categories` | 10 | Top 10 sub-categories kept per parent category; rest mapped to `"other"` |
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| `min-subcat-count` | 50 | Sub-categories with fewer than 50 products mapped to `"other"` |
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| Remove "other" events | — | All `"other"` events stripped; `time_since_last_event` recomputed |
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| `max-dominant-ratio` | 0.30 | Drop sequences where any single type exceeds 30% of events |
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| `max-repeat-ratio` | 0.35 | Drop sequences where consecutive same-type rate exceeds 35% |
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## Event Text
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Each event's `type_text`
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> The user reviewed
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The model must learn to predict the product sub-category from review content, product titles, and rating patterns.
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## Example
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```json
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{
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"seq_idx": 0,
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"seq_len":
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"type_category": "Electronics",
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"span_weeks":
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"description": "Amazon Electronics review timeline for a user spanning
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"metadata": "{\"user_id\": \"
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"time_since_start": [0.0, 0.
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"time_since_last_event": [0.0, 0.
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"type_event": ["
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"type_text": ["The user reviewed
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}
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```
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- marked-temporal-point-process
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size_categories:
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- n<1K
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---
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# Amazon Product Review Events
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- **Source:** [Amazon Reviews 2023](https://huggingface.co/datasets/McAuley-Lab/Amazon-Reviews-2023) (McAuley Lab, HuggingFace)
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- **Categories:** Electronics, Books, Home & Kitchen, Beauty & Personal Care
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- **Grouping:** Events grouped by user within a single product category
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- **Sequences:** 311
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- **Sequence length:** 50–78 events per sequence (mean: 58.3)
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- **Event types:** 40 sub-categories (no "other")
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- **Time unit:** weeks
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## Schema
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| `time_since_start` | list[float] | Time since the first event (in weeks) |
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| `time_since_last_event` | list[float] | Time since the previous event (in weeks) |
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| `type_event` | list[str] | Product sub-category slug (see below) |
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| `type_text` | list[str] | Natural language description with full category path (excluding root), product title, star rating, review title, and review body. HTML tags, entities, embedded media tags, and URLs are stripped. |
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## Event Types (40 sub-categories)
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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.
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**Home & Kitchen** (211 sequences, 10 types): `kitchen_dining` (2,821), `home_d_cor_products` (2,504), `bedding` (2,033), `furniture` (1,343), `bath` (1,106), `storage_organization` (1,061), `heating_cooling_air_quality` (390), `wall_art` (345), `seasonal_d_cor` (304), `event_party_supplies` (239)
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**Beauty & Personal Care** (76 sequences, 10 types): `hair_care` (928), `skin_care` (895), `makeup` (892), `tools_accessories` (656), `foot_hand_nail_care` (609), `personal_care` (206), `shave_hair_removal` (160), `fragrance` (144), `salon_spa` (90), `men_s_grooming` (10)
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**Electronics** (18 sequences, 10 types): `computers_accessories` (224), `television_video` (183), `home_audio` (142), `camera_photo` (110), `portable_audio_video` (105), `headphones_earbuds_accessories` (79), `car_vehicle_electronics` (74), `accessories_supplies` (65), `security_surveillance` (59), `gps_finders_accessories` (9)
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**Books** (6 sequences, 10 types): `literature_fiction` (82), `history` (56), `biographies_memoirs` (41), `children_s_books` (40), `mystery_thriller_suspense` (30), `teen_young_adult` (23), `arts_photography` (22), `christian_books_bibles` (18), `crafts_hobbies_home` (12), `politics_social_sciences` (8)
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## Curation Filters
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| `max-events` | 80 | Max reviews per user sequence |
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| `min-types` | 3 | At least 3 distinct sub-category types |
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| `min-span` | 6 months | Exclude sequences spanning less than 6 months |
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| `max-span` | 72 months | Exclude sequences spanning 72+ months |
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| `max-sub-categories` | 10 | Top 10 sub-categories kept per parent category; rest mapped to `"other"` |
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| `min-subcat-count` | 50 | Sub-categories with fewer than 50 products mapped to `"other"` |
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| Verified purchase only | — | Only verified purchase reviews are included |
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| Exclude catch-all categories | — | Products without proper sub-category hierarchy (e.g., "All Electronics") are excluded |
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| Remove "other" events | — | All `"other"` events stripped; `time_since_last_event` recomputed |
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| `max-dominant-ratio` | 0.30 | Drop sequences where any single type exceeds 30% of events |
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| `max-repeat-ratio` | 0.35 | Drop sequences where consecutive same-type rate exceeds 35% |
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## Event Text
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Each event's `type_text` is a natural language sentence containing the full category path (excluding the root category), product title, star rating, and review content:
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> The user reviewed "Instant Pot Duo 7-in-1 Electric Pressure Cooker" under Kitchen & Dining > Small Appliances > Multi-Cookers, rating it 5 out of 5 stars. Their review titled "Best kitchen purchase ever" says: This has completely changed how I cook dinner. The pressure cooking function is incredible and saves so much time.
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The model must learn to predict the product sub-category from review content, product titles, category paths, and rating patterns.
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## Example
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```json
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{
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"seq_idx": 0,
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"seq_len": 61,
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"type_category": "Electronics",
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"span_weeks": 269.34,
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"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.",
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"metadata": "{\"user_id\": \"AF7S6K2L...\", \"parent_category\": \"Electronics\", \"num_reviews\": 76, \"num_sub_categories\": 10, ...}",
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"time_since_start": [0.0, 0.0016, 0.0024, ...],
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"time_since_last_event": [0.0, 0.0016, 0.0008, ...],
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"type_event": ["camera_photo", "computers_accessories", "accessories_supplies", ...],
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"type_text": ["The user reviewed \"D-Link DCS-5222L HD Pan & Tilt Wi-Fi Camera\" under Camera & Photo > Video Surveillance > Surveillance Cameras > Dome Cameras, rating it 5 out of 5 stars. ...", ...]
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}
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```
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