Date : Jeudi | 2024-02-01 à 12h30
Lieu : Salle des thèses
Julien M'BARKI (Chaire PcEn, Université Paris 1 Panthéon-Sorbonne)
Music streaming services make massive use of algorithms in their music recommender systems (MRS) to guide users to tracks they are likely to enjoy. However, the black-box nature of these algorithms makes them difficult for users to understand, both in terms of how they work and the music they predict. The field of explainable AI (XAI), and in particular its “explanation” side, has emerged to make the uses of AI (including MRSs) more comprehensible to users. This paper aims to observe, using an experimental method, whether the explanation of an MRS algorithm induces a change in listening behavior on music streaming services. In a theoretical framework, we model two types of users' behaviors, namely “study” and “browse” behaviors. We then test in the lab the explanation effects on these behaviors by explaining two simplified MRSs, taking into account only certain music recommendation criteria.