February 17, 2022 Dataset Open Access

Kowald Dominik

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  <identifier identifierType="DOI">10.5281/zenodo.6123879</identifier>
  <creators>
    <creator>
      <creatorName>Kowald Dominik</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="http://orcid.org/">0000-0003-3230-6234</nameIdentifier>
      <affiliation>Know-Center GmbH, TU Graz</affiliation>
    </creator>
  </creators>
  <titles>
    <title>Fair RecSys Datasets</title>
  </titles>
  <publisher>Zenodo</publisher>
  <publicationYear>2022</publicationYear>
  <subjects>
    <subject>multimedia recommender systems</subject>
    <subject>fairness</subject>
    <subject>popularity bias</subject>
  </subjects>
  <dates>
    <date dateType="Issued">2022-02-17</date>
  </dates>
  <language>en</language>
  <resourceType resourceTypeGeneral="Dataset"/>
  <alternateIdentifiers>
    <alternateIdentifier alternateIdentifierType="url">https://zenodo.org/record/6123879</alternateIdentifier>
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  <version>1.0</version>
  <rightsList>
    <rights rightsURI="https://creativecommons.org/licenses/by/4.0/legalcode">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
  </rightsList>
  <descriptions>
    <description descriptionType="Abstract">&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;</description>
  </descriptions>
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