Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

TL;DR

This study examines popularity bias in multimedia recommender systems, revealing that unpopular items are underrepresented and users interested in less popular content receive poorer recommendations.

Contribution

It introduces a detailed analysis of popularity bias across multiple datasets and user groups, highlighting its impact on recommendation fairness and quality.

Findings

Users less interested in popular items have larger profiles and are key data sources.

Popular items are recommended more frequently than unpopular ones.

Users interested in less popular items receive worse recommendations.

Abstract

Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative filtering. In this paper, we investigate a potential issue of such collaborative-filtering based multimedia recommender systems, namely popularity bias that leads to the underrepresentation of unpopular items in the recommendation lists. Therefore, we study four multimedia datasets, i.e., LastFm, MovieLens, BookCrossing and MyAnimeList, that we each split into three user groups differing in their inclination to popularity, i.e., LowPop, MedPop and HighPop. Using these user groups, we evaluate four collaborative filtering-based algorithms with respect to popularity bias on the item and the user level. Our findings are three-fold: firstly, we show that users with little interest into popular items tend to have large user profiles and thus, are important data sources for multimedia recommender systems. Secondly, we find that popular items are recommended more frequently than unpopular ones. Thirdly, we find that users with little interest into popular items receive significantly worse recommendations than users with medium or high interest into popularity.