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Research data . Dataset . 2022

Fair RecSys Datasets

Dominik, Kowald;
Open Access
English
Published: 17 Feb 2022
Publisher: Zenodo
Abstract
Four multimedia recommender systems datasets to study popularity bias and fairness: Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/) MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/) BookCrossing (book.zip), based on the BookCrossing dataset of Uni Freiburg (http://www2.informatik.uni-freiburg.de/~cziegler/BX/) MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database) 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 (https://github.com/domkowald/FairRecSys) and on Arxiv (https://arxiv.org/abs/2203.00376)
Subjects

multimedia recommender systems, fairness, popularity bias

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