User-centric evaluation of session-based recommendations for an automated radio station

Abstract

The creation of an automated and virtually endless playlist given a start item is a common feature of modern media streaming services. When no past information about the user's preferences is available, the creation of such playlists can be done using session-based recommendation techniques. In this case, the recommendations only depend on the start item and the user's interactions in the current listening session, such as "liking" or skipping an item. In recent years, various novel session-based techniques were proposed, often based on deep learning. The evaluation of such approaches is in most cases solely based on offline experimentation and abstract accuracy measures. However, such evaluations cannot inform us about the quality as perceived by users. To close this research gap, we have conducted a user study (N=250), where the participants interacted with an automated online radio station. Each treatment group received recommendations that were generated by one of five different algorithms. Our results show that comparably simple techniques led to quality perceptions that are similar or even better than when a complex deep learning mechanism or Spotify's recommendations are used. The simple mechanisms, however, often tend to recommend comparably popular tracks, which can lead to lower discovery effects.