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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:

  1. User Reviews (ratings, text, helpfulness votes, etc.);
  2. Item Metadata (descriptions, price, raw image, etc.);
  3. Links (user-item / bought together graphs).

What's New?

In the Amazon Reviews'23, we provide:

  1. Larger Dataset: We collected 571.54M reviews, 245.2% larger than the last version;
  2. Newer Interactions: Current interactions range from May. 1996 to Sep. 2023;
  3. Richer Metadata: More descriptive features in item metadata;
  4. Fine-grained Timestamp: Interaction timestamp at the second or finer level;
  5. Cleaner Processing: Cleaner item metadata than previous versions;
  6. 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.

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.