A User-Centered Investigation of Personal Music Tours

Abstract

Streaming services use recommender systems to surface the right music to users. Playlists are a popular way to present music in a list-like fashion, i.e. as a plain list of songs. An alternative are tours, where the songs alternate with segues, which explain the connections between consecutive songs. Tours address the user need of seeking background information about songs, and are found to be superior to playlists, given the right user context. In this work, we provide, for the first time, a user-centered evaluation of two tour-generation algorithms (G and O ) using semi-structured interviews. We assess the algorithms, we discuss attributes of the tours that the algorithms produce, we identify which attributes are desirable and which are not, and we enumerate several possible improvements to the algorithms, along with practical suggestions on how to implement the improvements. Our main findings are that G generates more likeable tours than O , and that three important attributes of tours are segue diversity, song arrangement and song familiarity. More generally, we provide insights into how to present music to users, which could inform the design of user-centered recommender systems.

Beyond the Trends: Evolution and Future Directions in Music Recommender Systems Research

“…Factors such as injecting novelty into recommendations, ensuring regular updates, and catering to users' interest in rating contribute significantly to fostering a positive UX [64]. Furthermore, the dynamics of interaction between music listeners and platforms, coupled with elements like satisfaction levels, user activity, and feedback, exert considerable influence on UX and shape listening preferen [65]. Notably, user-centered evaluations of tourgeneration algorithms offer valuable insights into optimal music presentation methods and avenues for enhancing recommender systems [66].…”