Emotion-aware Personalized Music Recommendation with a Heterogeneity-aware Deep Bayesian Network

TL;DR

This paper introduces a heterogeneity-aware deep Bayesian network for emotion-aware personalized music recommendation, effectively modeling diverse user emotions and music preferences to improve recommendation accuracy.

Contribution

It proposes a novel Heterogeneity-aware Deep Bayesian Network that captures multiple types of heterogeneity in emotion and music preferences, advancing personalized music recommendation systems.

Findings

Significantly outperforms baseline methods on key metrics

Effectively models user emotion and music preference heterogeneity

Validated through extensive experiments and case studies

Abstract

Music recommender systems play a critical role in music streaming platforms by providing users with music that they are likely to enjoy. Recent studies have shown that user emotions can influence users' preferences for music moods. However, existing emotion-aware music recommender systems (EMRSs) explicitly or implicitly assume that users' actual emotional states expressed through identical emotional words are homogeneous. They also assume that users' music mood preferences are homogeneous under the same emotional state. In this article, we propose four types of heterogeneity that an EMRS should account for: emotion heterogeneity across users, emotion heterogeneity within a user, music mood preference heterogeneity across users, and music mood preference heterogeneity within a user. We further propose a Heterogeneity-aware Deep Bayesian Network (HDBN) to model these assumptions. The HDBN mimics a user's decision process of choosing music with four components: personalized prior user emotion distribution modeling, posterior user emotion distribution modeling, user grouping, and Bayesian neural network-based music mood preference prediction. We constructed two datasets, called EmoMusicLJ and EmoMusicLJ-small, to validate our method. Extensive experiments demonstrate that our method significantly outperforms baseline approaches on metrics of HR, Precision, NDCG, and MRR. Ablation studies and case studies further validate the effectiveness of our HDBN. The source code and datasets are available at https://github.com/jingrk/HDBN.