6123879
doi
10.5281/zenodo.6123879
oai:zenodo.org:6123879
Fair RecSys Datasets
Kowald Dominik
Know-Center GmbH, TU Graz
info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
multimedia recommender systems
fairness
popularity bias
<p>Four multimedia recommender systems datasets to study popularity bias and fairness:</p>
<ol>
<li>Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)</li>
<li>MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)</li>
<li>BookCrossing (book.zip), based on the BookCrossing dataset of Uni Freiburg (http://www2.informatik.uni-freiburg.de/~cziegler/BX/)</li>
<li>MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)</li>
</ol>
<p>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).</p>
<p>The format of the three user files are "user,mainstreaminess"</p>
<p>The format of the user-events files are "user,item,preference"</p>
<p>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)</p>
<p> </p>
<p> </p>
Zenodo
2022-02-17
info:eu-repo/semantics/other
6123878
2.0
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https://zenodo.org/records/6123879/files/ml.zip
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https://zenodo.org/records/6123879/files/anime.zip
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isVersionOf
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