The Unfairness of Popularity Bias in Recommendation

TL;DR

This paper investigates how popularity bias in recommender systems affects different user groups, revealing that recommendations often overly favor popular items regardless of user preferences, thus deviating from user expectations.

Contribution

It introduces a user-centric perspective on popularity bias, categorizing users by their interest in popular items and analyzing bias impact across these groups.

Findings

Recommendations are heavily skewed towards popular items.

User preferences for niche items are often ignored.

Bias disparity varies significantly among user groups.

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.