Towards a content-based prediction of personalized musical preferences using transfer learning

Music recommender systems attempt to provide to the users tracks in accordance with their preferences. Thus, content-based recommender systems rely on the audio signal in order to infer users' preferences. We present a preliminary study of a personalized recommendation task which is here considered as a prediction between two classes: `like' and `dislike'. First, a categorization experiment was conducted in order to generate users' preference data. Each volunteer has sorted pieces of music according to whether he liked those or not, by creating personal playlists (around 10 hours per volunteer) on a music streaming platform. Pieces of music were either coming from the free browsing of the volunteer on the platform or from a corpus of pieces that was built according to musicological criteria. We used an inductive transfer learning with music genre classification as a source task and the prediction of `like' and `dislike' classes as a target task. We pre-trained our models with two different corpora: GTZAN and FMA. Cross-validation results obtained are promising, with very satisfying results (more than 80% satisfaction) for 14 volunteers out of 20. This method could be used for a music recommendation purpose and further research will be conducted in this direction.