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Research data . Dataset . 2022

Fair RecSys Datasets

Dominik, Kowald;
Open Access
Published: 17 Feb 2022
Publisher: Zenodo
Four multimedia recommender systems datasets to study popularity bias and fairness: (, based on the LFM-1b dataset of JKU Linz ( MovieLens (, based on MovieLens-1M dataset ( BookCrossing (, based on the BookCrossing dataset of Uni Freiburg ( MyAnimeList (, based on the MyAnimeList dataset of Kaggle ( Each dataset contains of user interactions (user_events.txt) and three user groups that differ in their inclination to popular/mainstream items: LowPop (low_main_users.txt), MedPop (med_main_users.txt), and HighPop (high_main_users.txt). The format of the three user files are "user,mainstreaminess" The format of the user-events files are "user,item,preference" Example Python-code for analyzing the datasets as well as more information on the user groups can be found on Github ( and on Arxiv (

multimedia recommender systems, fairness, popularity bias