The Potential of the Confluence of Theoretical and Algorithmic Modeling in Music Recommendation

TL;DR

This paper advocates for integrating theoretical and algorithmic modeling approaches to improve music recommendation systems by addressing contextual, subjective, and bias-related challenges.

Contribution

It highlights the potential benefits of combining theoretical explanations with algorithmic predictions in music recommendation research.

Findings

Identifies key challenges in current music recommendation datasets and methods.

Argues that combining theoretical and algorithmic models can enhance recommendation quality.

Emphasizes the importance of explanations and bias mitigation in recommender systems.

Abstract

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.