  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.
