Automatic Music Recommendation Based on Acoustic Content and Implicit Listening Feedback.

Borges, Rodrigo C.; Queiroz, Marcelo

Recommending music automatically isn't simply about finding songs similar to what a user is accustomed to listen, but also about suggesting potentially interesting pieces that bear no obvious relationships to a user listening history. This work addresses the problem known as "cold start", where new songs with no user listening history are added to an existing dataset, and proposes a probabilistic model for inference of users listening interest on newly added songs based on acoustic content and implicit listening feedback. Experiments using a dataset of selected Brazilian popular music show that the proposed method compares favorably to alternative statistical models.

MECHANICAL musical instruments; ACOUSTIC (Music); MUSIC showcases; PROBABILISTIC databases; MUSICOLOGY

Revista Música Hodie, 2018, Vol 18, Issue 1, p31

1676-3939

Academic Journal

10.5216/mh.v18i1.53569