The Potential of the Confluence of Theoretical and Algorithmic Modeling in Music Recommendation
It tackles the problem of improving music recommender systems for users and developers, but it is incremental as it builds on existing ideas without presenting new results.
This position paper addresses the challenges in music recommendation, such as limited contextual data and the need for multi-criteria evaluation, and argues that combining theoretical and algorithmic modeling is a promising approach.
The task of a music recommender system is to predict what music item a particular user would like to listen to next. This position paper discusses the main challenges of the music preference prediction task: the lack of information on the many contextual factors influencing a user's music preferences in existing open datasets, the lack of clarity of what the right choice of music is and whether a right choice exists at all; the multitude of criteria (beyond accuracy) that have to be met for a "good" music item recommendation; and the need for explanations on relationships to identify (and potentially counteract) unwanted biases in recommendation approaches. The paper substantiates the position that the confluence of theoretical modeling (which seeks to explain behaviors) and algorithmic modeling (which seeks to predict behaviors) seems to be an effective avenue to take in computational modeling for music recommender systems.