Audio Prototypical Network for Controllable Music Recommendation

Fırat Öncel, Emiliano Penaloza, Haolun Wu, Shubham Gupta, Mirco Ravanelli, Laurent Charlin, Cem Subakan

Keywords: Scrutable Recommender System, Controllability, Explainable Machine Learning

Abstract:

Traditional recommendation systems represent user preferences in dense representations obtained through black-box encoder models. While these models often provide strong recommendation performance, they lack interpretability for users, leaving users unable to understand or control the system’s modeling of their preferences. This limitation is especially challenging in music recommendation, where user preferences are highly personal and often evolve based on nuanced qualities like mood, genre, tempo, or instrumentation.
In this paper, we propose an audio prototypical network for controllable music recommendation. This network expresses user preferences in terms of prototypes representative of semantically meaningful features pertaining to musical qualities. We show that the model obtains competitive recommendation performance compared to popular baseline models while also providing interpretable and controllable user profiles.

Primary Area: interpretability and explainable AI

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Submission Number: 8055