Datasets For Recommender Systems

This is a repository of public data sources for Recommender Systems (RS).

All of these recommendation datasets can convert to the atomic files defined in RecBole, which is a unified, comprehensive and efficient recommendation library.

After converting to the atomic files, you can use RecBole to test the performance of different recommender models on these datasets easily. For more information about RecBole, please refer to RecBole.

Usage

In order to use RecBole, you need to convert these original datasets to the atomic file which is a kind of data format defined by RecBole.

We provide two ways to convert these datasets into atomic files:

Download the raw dataset and process it with conversion tools we provide in this repository. Please refer to conversion tools.
Directly download the processed atomic files. Baidu Wangpan (Password: e272), Google Drive.

Datasets link and brief introduction

Shopping

Amazon: Amazon Review Data includes reviews (ratings, text, helpfulness votes) and product metadata (descriptions, category information, price, brand, and image features), which includes a previous version in 2014 and an updated version in 2018. Our processed datasets are detailed here.
- Amazon 2014: This dataset contains product reviews and metadata from Amazon, including 24 categories and 142.8 million reviews spanning May 1996 - July 2014.
- Amazon 2018: This Dataset is an updated version of the Amazon review dataset released in 2014. The total number of reviews is 233.1 million and the number of categories is 29 (142.8 million and 24 in 2014) and current data includes reviews in the range May 1996 - Oct 2018.
- Amazon 2023: This Dataset is the latest version of the Amazon review dataset released in 2014. The total number of reviews is 571.54 million and the number of categories is 33 and current data includes reviews in the range May 1996 - Sep 2023.
Amazon_M2: This dataset is a collection of anonymized customer sessions containing products from six different locales: English, German, Japanese, French, Italian, and Spanish.
Alibaba-iFashion: This dataset is a fashion outfit dataset collected from Alibaba online shopping systems in the paper POG. The items from each outfit are viewed as the items being recommended to users, where each item consists of attributes such as category and title.
Epinions: This dataset was collected from Epinions.com, a popular online consumer review website. It contains trust relationships amongst users and spans more than a decade, from January 2001 to November 2013.
Yelp: This dataset was collected from Yelp. The Yelp dataset is a subset of our businesses, reviews, and user data for use in personal, educational, and academic purposes. Starting from Yelp Challenge 2018 (the original link to this competition is not found and there will not be another round of Yelp Dataset Challenge), there are four versions of Yelp datasets in total and Yelp has also posted the dataset on Kaggle, where you can also download a few earlier versions. Our processed 5 datasets are detailed here.
- Yelp 2018: It is the first version of Yelp dataset released in Yelp Challenge 2018 including 5,261,669 reviews.
- Yelp 2020: It is the second version of Yelp dataset released in 2020, including 8,021,122 reviews.
- Yelp 2021: It is the first version of Yelp dataset released in 2021, including 8,635,403 reviews.
- Yelp 2022: It is the latest version of Yelp dataset, which contains 908,915 tips by 1,987,897 users over 1.2 million business attributes like hours, parking, availability, and ambience aggregated check-ins over time for each of the 131,930 businesses.
- Yelp-full: This is a combination dataset including four versions of yelp datasets mentioned above, where the duplicates are dropped and the number of total reviews is 28,908,240.
Tmall: This dataset is provided by Ant Financial Services, using in the IJCAI16 contest.
DIGINETICA: The dataset includes user sessions extracted from an e-commerce search engine logs, with anonymized user ids, hashed queries, hashed query terms, hashed product descriptions and meta-data, log-scaled prices, clicks, and purchases.
YOOCHOOSE: This dataset was constructed by YOOCHOOSE GmbH to support participants in the RecSys Challenge 2015.
Retailrocket: The data has been collected from a real-world ecommerce website. It is raw data, i.e. without any content transformations, however, all values are hashed due to confidential issues.
Ta Feng: The dataset contains a Chinese grocery store transaction data from November 2000 to February 2001.

Advertising

Criteo: This dataset was collected from Criteo, which consists of a portion of Criteo's traffic over a period of several days.
Avazu: This dataset is used in Avazu CTR prediction contest.
iPinYou: This dataset was provided by iPinYou, which contains all training datasets and leaderboard testing datasets of the three seasons iPinYou Global RTB(Real-Time Bidding) Bidding Algorithm Competition.
AliEC: Ali_Display_Ad_Click is a dataset of click rate prediction about display Ad, which is displayed on the website of Taobao. The dataset is offered by the company of Alibaba.

Check-in

Foursquare: This dataset contains check-ins in NYC and Tokyo collected for about 10 month. Each check-in is associated with its time stamp, its GPS coordinates and its semantic meaning.
Gowalla: This dataset is from a location-based social networking website where users share their locations by checking-in, and contains a total of 6,442,890 check-ins of these users over the period of Feb. 2009 - Oct. 2010.

Movies

MovieLens: GroupLens Research has collected and made available rating datasets from their movie website.
Netflix: This is the official data set used in the Netflix Prize competition.
Douban: Douban Movie is a Chinese website that allows Internet users to share their comments and viewpoints about movies. This dataset contains more than 2 million short comments of 28 movies in Douban Movie website.
Twitch: This is a dataset of users consuming streaming content on Twitch. We retrieved all streamers, and all users connected in their respective chats, every 10 minutes during 43 days.
- Twitch-100k: Twitch-100k is a subset of 100k users for benchmark purposes. The code is available in this Github repository.
- Twitch-full: See the Google Drive folder containing all Twitch files. Twitch-full contains the full dataset while Twitch-100k is a subset.

Music

Last.FM: This dataset contains social networking, tagging, and music artist listening information from a set of 2K users from Last.fm online music system.
LFM-1b: This dataset contains more than one billion music listening events created by more than 120,000 users of Last.FM. Each listening event is characterized by artist, album, and track name, and includes a timestamp.
Yahoo Music: This dataset represents a snapshot of the

RecSysDatasets

Install / Use

README