The Unfairness of Popularity Bias in Recommendation

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, Bamshad Mobasher

Research output: Contribution to conferencePaperAcademic

6 Citations (Scopus)

Abstract

Recommender systems are known to suffer from the popularity bias problem: popular (i.e. frequently rated) items get a lot of exposure while less popular ones are under-represented in the recommendations. Research in this area has been mainly focusing on finding ways to tackle this issue by increasing the number of recommended long-tail items or otherwise the overall catalog coverage. In this paper, however, we look at this problem from the users’ perspective: we want to see how popularity bias causes the recommendations to deviate from what the user expects to get from the recommender system. We define three different groups of users according to their interest in popular items (Niche, Diverse and Blockbuster-focused) and show the impact of popularity bias on the users in each group. Our experimental results on a movie dataset show that in many recommendation algorithms the recommendations the users get are extremely concentrated on popular items even if a user is interested in long-tail and non-popular items showing an extreme bias disparity.

Original languageEnglish
Publication statusPublished - 2019
EventRecSys Workshop on Recommendation in Multistakeholder Environments (RMSE) -
Duration: 20 Sep 2019 → …

Workshop

WorkshopRecSys Workshop on Recommendation in Multistakeholder Environments (RMSE)
Period20/09/19 → …

Keywords

  • Fairness
  • Long-tail recommendation
  • Popularity bias
  • Recommender systems

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