research-article

Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation

Authors:

Shuiguang DengAuthors Info & Claims

ACM Transactions on Information Systems, Volume 42, Issue 1

Article No.: 16, Pages 1 - 30

https://doi.org/10.1145/3592853

Published: 18 August 2023 Publication History

Abstract

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.

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Cited By

Jiang JGuo PXu XWu JCui Y(2024)Social Perception with Graph Attention Network for RecommendationACM Transactions on Recommender Systems10.1145/3665503Online publication date: 21-May-2024
https://doi.org/10.1145/3665503
An JGao MTang J(2024)MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI RecommendationACM Transactions on Information Systems10.1145/3664651Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3664651
Yan MLiu FSun JSun FCheng ZHan YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657696(946-955)Online publication date: 10-Jul-2024
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Index Terms

Multi-View Enhanced Graph Attention Network for Session-Based Music Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 42, Issue 1

January 2024

924 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3613513

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 18 August 2023

Online AM: 20 May 2023

Accepted: 12 April 2023

Revised: 12 February 2023

Received: 07 June 2022

Published in TOIS Volume 42, Issue 1

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Jiang JGuo PXu XWu JCui Y(2024)Social Perception with Graph Attention Network for RecommendationACM Transactions on Recommender Systems10.1145/3665503Online publication date: 21-May-2024
https://doi.org/10.1145/3665503
An JGao MTang J(2024)MvStHgL: Multi-view Hypergraph Learning with Spatial-temporal Periodic Interests for Next POI RecommendationACM Transactions on Information Systems10.1145/3664651Online publication date: 10-May-2024
https://dl.acm.org/doi/10.1145/3664651
Yan MLiu FSun JSun FCheng ZHan YHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Behavior-Contextualized Item Preference Modeling for Multi-Behavior RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657696(946-955)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657696
Zheng XLi XChen ZSun LYu QGuo LLuo Y(2024)Enhanced Self-Attention Mechanism for Long and Short Term Sequential Recommendation ModelsIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33667718:3(2457-2466)Online publication date: Jul-2024
https://doi.org/10.1109/TETCI.2024.3366771
Luan XZhong R(2024)Use Intention of Mobile Music and Audio Platforms Among Middle-Aged and Older Chinese AdultsInternational Journal of Human–Computer Interaction10.1080/10447318.2024.2338665(1-16)Online publication date: 15-Apr-2024
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Wang XWang DYu DWu RYang QDeng SXu G(2023)Intent-aware Graph Neural Network for Point-of-Interest embedding and recommendationNeurocomputing10.1016/j.neucom.2023.126734557:COnline publication date: 7-Nov-2023
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