February 17, 2022 Dataset Open Access

Kowald Dominik

Dublin Core Export

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  <dc:creator>Kowald Dominik</dc:creator>
  <dc:date>2022-02-17</dc:date>
  <dc:description>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)

 

 </dc:description>
  <dc:identifier>https://zenodo.org/record/6123879</dc:identifier>
  <dc:identifier>10.5281/zenodo.6123879</dc:identifier>
  <dc:identifier>oai:zenodo.org:6123879</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>doi:10.5281/zenodo.6123878</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>multimedia recommender systems</dc:subject>
  <dc:subject>fairness</dc:subject>
  <dc:subject>popularity bias</dc:subject>
  <dc:title>Fair RecSys Datasets</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>