McAuley-Lab/Amazon-Reviews-2023

Amazon Reviews 2023

Please also visit amazon-reviews-2023.github.io/ for more details, loading scripts, and preprocessed benchmark files.

[April 7, 2024] We add two useful files:

1. all_categories.txt: 34 lines (33 categories + "Unknown"), each line contains a category name.
2. asin2category.json: A mapping between parent_asin (item ID) to its corresponding category name.

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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.

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.