Automatic Music Recommendation Based on Acoustic Content and Implicit Listening Feedback
DOI:
https://doi.org/10.5216/mh.v18i1.53569Palabras clave:
Music recommendation systems, Cold start, Collaborative filtering, Content-based recommendation, Codeword bernoulli average model, Vector quantizationResumen
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 listen- ing 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 Bra- zilian popular music show that the proposed method compares favorably to alternative statistical models.
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