From the Beatles to Billie Eilish: Connecting Provider Representativeness and Exposure in Session-Based Recommender Systems

Session-based recommender systems consider the evolution of user preferences in browsing sessions. Existing studies suggest as next item the one that keeps the user engaged as long as possible. This point of view does not account for the providers’ perspective. In this paper, we highlight side effects over the providers caused by state-of-the-art models. We focus on the music domain and study how artists’ exposure in the recommendation lists is affected by the input data structure, where different session lengths are explored. We consider four session-based systems on three types of datasets, with long, short, and mixed playlist length. We provide measures to characterize disparate treatment between the artists, through a systematic analysis by comparing (i) the exposure received by an artist in the recommendations and (ii) their input representation in the data. Results show that artists for which we can observe a lot of interactions, but offering less items, are mistreated in terms of exposure. Moreover, we show how input data structure may impact the algorithms’ effectiveness, possibly due to preference-shift phenomena