Multi-view enhanced graph attention network for session-based music recommendation

Dongjing WANG, Xin ZHANG, Yuyu Yin, Dongjin YU, Guandong XU, Shuiguang DENG

Research output: Contribution to journalArticlespeer-review

10 Citations (Scopus)


Traditional music recommender systems are mainly based on users' interactions, which limit their performance. Particularly, various kinds of content information, such as metadata and description can be used to improve music recommendation. However, it remains to be addressed how to fully incorporate the rich auxiliary/side information and effectively deal with heterogeneity in it. In this paper, we propose a Multi-view Enhanced Graph Attention Network (named MEGAN) for session-based music recommendation. MEGAN can learn informative representations (embeddings) of music pieces and users from heterogeneous information based on graph neural network and attention mechanism. Specifically, the proposed approach MEGAN firstly models users' listening behaviors and the textual content of music pieces with a Heterogeneous Music Graph (HMG). Then, a devised Graph Attention Network is used to learn the low-dimensional embedding of music pieces and users and by integrating various kinds of information, which is enhanced by multi-view from HMG in an adaptive and unified way. Finally, users' hybrid preferences are learned from users' listening behaviors and music pieces that satisfy users real-time requirements are recommended. Comprehensive experiments are conducted on two real-world datasets, and the results show that MEGAN achieves better performance than baselines, including several state-of-the-art recommendation methods. Copyright © 2023 held by the owner/author(s). Publication rights licensed to ACM.

Original languageEnglish
Article number16
JournalACM Transactions on Information Systems
Issue number1
Publication statusPublished - Aug 2023


Wang, D., Zhang, X., Yin, Y., Yu, D., Xu, G., & Deng, S. (2023). Multi-view enhanced graph attention network for session-based music recommendation. ACM Transactions on Information Systems, 42(1), Article 16.


Dive into the research topics of 'Multi-view enhanced graph attention network for session-based music recommendation'. Together they form a unique fingerprint.