  Music recommender systems are crucial in music streaming platforms, providing
users with music they would enjoy. Recent studies have shown that user emotions
can affect users' music mood preferences. However, existing emotion-aware music
recommender systems (EMRSs) explicitly or implicitly assume that users' actual
emotional states expressed by an identical emotion word are homogeneous. They
also assume that users' music mood preferences are homogeneous under an
identical emotional state. In this article, we propose four types of
heterogeneity that an EMRS should consider: 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 to choose 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 a
large-scale dataset called EmoMusicLJ to validate our method. Extensive
experiments demonstrate that our method significantly outperforms baseline
approaches on widely used HR and NDCG recommendation metrics. Ablation
experiments and case studies further validate the effectiveness of our HDBN.
The source code is available at https://github.com/jingrk/HDBN.
