Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems

Dominik Kowald, Emanuel Lacic

Abstract:

Multimedia recommender systems suggest media items, e.g.,
songs, (digital) books and movies, to users by utilizing concepts of tra-
ditional recommender systems such as collaborative filtering. In this pa-
per, 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., Last.fm, Movie-
Lens, 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