February 17, 2022 Dataset Open Access

Kowald Dominik

DCAT Export

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        <foaf:name>Kowald Dominik</foaf:name>
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    <dcat:keyword>multimedia recommender systems</dcat:keyword>
    <dcat:keyword>fairness</dcat:keyword>
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    <dct:description>&lt;p&gt;Four multimedia recommender systems datasets to study popularity bias and fairness:&lt;/p&gt; &lt;ol&gt; &lt;li&gt;Last.fm (lfm.zip), based on the LFM-1b dataset of JKU Linz (http://www.cp.jku.at/datasets/LFM-1b/)&lt;/li&gt; &lt;li&gt;MovieLens (ml.zip), based on MovieLens-1M dataset (https://grouplens.org/datasets/movielens/1m/)&lt;/li&gt; &lt;li&gt;BookCrossing (book.zip), based on the BookCrossing dataset of Uni Freiburg (http://www2.informatik.uni-freiburg.de/~cziegler/BX/)&lt;/li&gt; &lt;li&gt;MyAnimeList (anime.zip), based on the MyAnimeList dataset of Kaggle (https://www.kaggle.com/CooperUnion/anime-recommendations-database)&lt;/li&gt; &lt;/ol&gt; &lt;p&gt;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).&lt;/p&gt; &lt;p&gt;The format of the three user files are &amp;quot;user,mainstreaminess&amp;quot;&lt;/p&gt; &lt;p&gt;The format of the user-events files are &amp;quot;user,item,preference&amp;quot;&lt;/p&gt; &lt;p&gt;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)&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;</dct:description>
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