---
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)}
}
```