User-centric Music Recommendations

TL;DR

This paper introduces a user-centric music recommendation framework that leverages long-term listening data, contextual information, and audio features to personalize and explain music suggestions, demonstrated through predicting danceability.

Contribution

The work presents a novel pipeline that integrates user context, music descriptors, and feature prediction to enhance personalized music recommendations with explainability.

Findings

Successfully predicted danceability from user context and tags

Created a large, long-term user listening dataset with rich metadata

Framework adaptable to multiple audio features and users

Abstract

This work presents a user-centric recommendation framework, designed as a pipeline with four distinct, connected, and customizable phases. These phases are intended to improve explainability and boost user engagement. We have collected the historical Last.fm track playback records of a single user over approximately 15 years. The collected dataset includes more than 90,000 playbacks and approximately 14,000 unique tracks. From track playback records, we have created a dataset of user temporal contexts (each row is a specific moment when the user listened to certain music descriptors). As music descriptors, we have used community-contributed Last.fm tags and Spotify audio features. They represent the music that, throughout years, the user has been listening to. Next, given the most relevant Last.fm tags of a moment (e.g. the hour of the day), we predict the Spotify audio features that best fit the user preferences in that particular moment. Finally, we use the predicted audio features to find tracks similar to these features. The final aim is to recommend (and discover) tracks that the user may feel like listening to at a particular moment. For our initial study case, we have chosen to predict only a single audio feature target: danceability. The framework, however, allows to include more target variables. The ability to learn the musical habits from a single user can be quite powerful, and this framework could be extended to other users.