metadata
language:
- en
tags:
- recommendation
- reviews
size_categories:
- 10B<n<100B
Amazon Reviews 2023
Please also visit amazon-reviews-2023.github.io/ for more details, loading scripts, and preprocessed benchmark files.
This is a large-scale Amazon Reviews dataset, collected in 2023 by McAuley Lab, and it includes rich features such as:
- User Reviews (ratings, text, helpfulness votes, etc.);
- Item Metadata (descriptions, price, raw image, etc.);
- Links (user-item / bought together graphs).
What's New?
In the Amazon Reviews'23, we provide:
- Larger Dataset: We collected 571.54M reviews, 245.2% larger than the last version;
- Newer Interactions: Current interactions range from May. 1996 to Sep. 2023;
- Richer Metadata: More descriptive features in item metadata;
- Fine-grained Timestamp: Interaction timestamp at the second or finer level;
- Cleaner Processing: Cleaner item metadata than previous versions;
- Standard Splitting: Standard data splits to encourage RecSys benchmarking.
Basic Statistics
We define the #R_Tokens as the number of tokens in user reviews and #M_Tokens as the number of tokens if treating the dictionaries of item attributes as strings. We emphasize them as important statistics in the era of LLMs.
We count the number of items based on user reviews rather than item metadata files. Note that some items lack metadata.
Compared to Previous Versions
| Year | #Review | #User | #Item | #R_Token | #M_Token | #Domain | Timespan |
|---|---|---|---|---|---|---|---|
| 2013 | 34.69M | 6.64M | 2.44M | 5.91B | -- | 28 | Jun'96 - Mar'13 |
| 2014 | 82.83M | 21.13M | 9.86M | 9.16B | 4.14B | 24 | May'96 - Jul'14 |
| 2018 | 233.10M | 43.53M | 15.17M | 15.73B | 7.99B | 29 | May'96 - Oct'18 |
| 2023 | 571.54M | 54.51M | 48.19M | 30.14B | 30.78B | 33 | May'96 - Sep'23 |
Grouped by Category
| Category | #User | #Item | #Rating | #R_Token | #M_Token | Download |
|---|---|---|---|---|---|---|
| All_Beauty | 632.0K | 112.6K | 701.5K | 31.6M | 74.1M | review, meta |
| Amazon_Fashion | 2.0M | 825.9K | 2.5M | 94.9M | 510.5M | review, meta |
| Appliances | 1.8M | 94.3K | 2.1M | 92.8M | 95.3M | review, meta |
| Arts_Crafts_and_Sewing | 4.6M | 801.3K | 9.0M | 350.0M | 695.4M | review, meta |
| Automotive | 8.0M | 2.0M | 20.0M | 824.9M | 1.7B | review, meta |
| Baby_Products | 3.4M | 217.7K | 6.0M | 323.3M | 218.6M | review, meta |
| Beauty_and_Personal_Care | 11.3M | 1.0M | 23.9M | 1.1B | 913.7M | review, meta |
| Books | 10.3M | 4.4M | 29.5M | 2.9B | 3.7B | review, meta |
| CDs_and_Vinyl | 1.8M | 701.7K | 4.8M | 514.8M | 287.5M | review, meta |
| Cell_Phones_and_Accessories | 11.6M | 1.3M | 20.8M | 935.4M | 1.3B | review, meta |
| Clothing_Shoes_and_Jewelry | 22.6M | 7.2M | 66.0M | 2.6B | 5.9B | review, meta |
| Digital_Music | 101.0K | 70.5K | 130.4K | 11.4M | 22.3M | review, meta |
| Electronics | 18.3M | 1.6M | 43.9M | 2.7B | 1.7B | review, meta |
| Gift_Cards | 132.7K | 1.1K | 152.4K | 3.6M | 630.0K | review, meta |
| Grocery_and_Gourmet_Food | 7.0M | 603.2K | 14.3M | 579.5M | 462.8M | review, meta |
| Handmade_Products | 586.6K | 164.7K | 664.2K | 23.3M | 125.8M | review, meta |
| Health_and_Household | 12.5M | 797.4K | 25.6M | 1.2B | 787.2M | review, meta |
| Health_and_Personal_Care | 461.7K | 60.3K | 494.1K | 23.9M | 40.3M | review, meta |
| Home_and_Kitchen | 23.2M | 3.7M | 67.4M | 3.1B | 3.8B | review, meta |
| Industrial_and_Scientific | 3.4M | 427.5K | 5.2M | 235.2M | 363.1M | review, meta |
| Kindle_Store | 5.6M | 1.6M | 25.6M | 2.2B | 1.7B | review, meta |
| Magazine_Subscriptions | 60.1K | 3.4K | 71.5K | 3.8M | 1.3M | review, meta |
| Movies_and_TV | 6.5M | 747.8K | 17.3M | 1.0B | 415.5M | review, meta |
| Musical_Instruments | 1.8M | 213.6K | 3.0M | 182.2M | 200.1M | review, meta |
| Office_Products | 7.6M | 710.4K | 12.8M | 574.7M | 682.8M | review, meta |
| Patio_Lawn_and_Garden | 8.6M | 851.7K | 16.5M | 781.3M | 875.1M | review, meta |
| Pet_Supplies | 7.8M | 492.7K | 16.8M | 905.9M | 511.0M | review, meta |
| Software | 2.6M | 89.2K | 4.9M | 179.4M | 67.1M | review, meta |
| Sports_and_Outdoors | 10.3M | 1.6M | 19.6M | 986.2M | 1.3B | review, meta |
| Subscription_Boxes | 15.2K | 641 | 16.2K | 1.0M | 447.0K | review, meta |
| Tools_and_Home_Improvement | 12.2M | 1.5M | 27.0M | 1.3B | 1.5B | review, meta |
| Toys_and_Games | 8.1M | 890.7K | 16.3M | 707.9M | 848.3M | review, meta |
| Video_Games | 2.8M | 137.2K | 4.6M | 347.9M | 137.3M | review, meta |
| Unknown | 23.1M | 13.2M | 63.8M | 3.3B | 232.8M | review, meta |
Check Pure ID files and corresponding data splitting strategies in Common Data Processing section.
Quick Start
Load User Reviews
from datasets import load_dataset
dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", "raw_review_All_Beauty", trust_remote_code=True)
print(dataset["full"][0])
{'rating': 5.0,
'title': 'Such a lovely scent but not overpowering.',
'text': "This spray is really nice. It smells really good, goes on really fine, and does the trick. I will say it feels like you need a lot of it though to get the texture I want. I have a lot of hair, medium thickness. I am comparing to other brands with yucky chemicals so I'm gonna stick with this. Try it!",
'images': [],
'asin': 'B00YQ6X8EO',
'parent_asin': 'B00YQ6X8EO',
'user_id': 'AGKHLEW2SOWHNMFQIJGBECAF7INQ',
'timestamp': 1588687728923,
'helpful_vote': 0,
'verified_purchase': True}
Load Item Metadata
dataset = load_dataset("McAuley-Lab/Amazon-Reviews-2023", "raw_meta_All_Beauty", split="full", trust_remote_code=True)
print(dataset[0])
{'main_category': 'All Beauty',
'title': 'Howard LC0008 Leather Conditioner, 8-Ounce (4-Pack)',
'average_rating': 4.8,
'rating_number': 10,
'features': [],
'description': [],
'price': 'None',
'images': {'hi_res': [None,
'https://m.media-amazon.com/images/I/71i77AuI9xL._SL1500_.jpg'],
'large': ['https://m.media-amazon.com/images/I/41qfjSfqNyL.jpg',
'https://m.media-amazon.com/images/I/41w2yznfuZL.jpg'],
'thumb': ['https://m.media-amazon.com/images/I/41qfjSfqNyL._SS40_.jpg',
'https://m.media-amazon.com/images/I/41w2yznfuZL._SS40_.jpg'],
'variant': ['MAIN', 'PT01']},
'videos': {'title': [], 'url': [], 'user_id': []},
'store': 'Howard Products',
'categories': [],
'details': '{"Package Dimensions": "7.1 x 5.5 x 3 inches; 2.38 Pounds", "UPC": "617390882781"}',
'parent_asin': 'B01CUPMQZE',
'bought_together': None,
'subtitle': None,
'author': None}
Check data loading examples and Huggingface datasets APIs in Common Data Loading section.
Data Fields
For User Reviews
| Field | Type | Explanation |
|---|---|---|
| rating | float | Rating of the product (from 1.0 to 5.0). |
| title | str | Title of the user review. |
| text | str | Text body of the user review. |
| images | list | Images that users post after they have received the product. Each image has different sizes (small, medium, large), represented by the small_image_url, medium_image_url, and large_image_url respectively. |
| asin | str | ID of the product. |
| parent_asin | str | Parent ID of the product. Note: Products with different colors, styles, sizes usually belong to the same parent ID. The “asin” in previous Amazon datasets is actually parent ID. Please use parent ID to find product meta. |
| user_id | str | ID of the reviewer |
| timestamp | int | Time of the review (unix time) |
| verified_purchase | bool | User purchase verification |
| helpful_vote | int | Helpful votes of the review |
For Item Metadata
| Field | Type | Explanation |
|---|---|---|
| main_category | str | Main category (i.e., domain) of the product. |
| title | str | Name of the product. |
| average_rating | float | Rating of the product shown on the product page. |
| rating_number | int | Number of ratings in the product. |
| features | list | Bullet-point format features of the product. |
| description | list | Description of the product. |
| price | float | Price in US dollars (at time of crawling). |
| images | list | Images of the product. Each image has different sizes (thumb, large, hi_res). The “variant” field shows the position of image. |
| videos | list | Videos of the product including title and url. |
| store | str | Store name of the product. |
| categories | list | Hierarchical categories of the product. |
| details | dict | Product details, including materials, brand, sizes, etc. |
| parent_asin | str | Parent ID of the product. |
| bought_together | list | Recommended bundles from the websites. |
Citation
@article{hou2024bridging,
title={Bridging Language and Items for Retrieval and Recommendation},
author={Hou, Yupeng and Li, Jiacheng and He, Zhankui and Yan, An and Chen, Xiusi and McAuley, Julian},
journal={arXiv preprint arXiv:2403.03952},
year={2024}
}
Contact Us
Report Bugs: To report bugs in the dataset, please file an issue on our GitHub.
Others: For research collaborations or other questions, please email yphou AT ucsd.edu.