Cited By
View all- Li QMa HJin WJi YLi Z(2024)Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendationExpert Systems with Applications10.1016/j.eswa.2024.124497255(124497)Online publication date: Dec-2024
Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation
Authors: 
Xuewei Li, Hongwei Chen, Jian Yu, Mankun Zhao, Tianyi Xu, Wenbin Zhang, Mei YuAuthors Info & Claims
Xuewei Li, Hongwei Chen, Jian Yu, Mankun Zhao, Tianyi Xu, Wenbin Zhang, Mei YuAuthors Info & ClaimsPages 387 - 395
Abstract
Multi-behavior sequential recommendation (MBSR) predicts a user's next item of interest based on their interaction history across different behavior types. Although existing studies have proposed capturing the correlation between different types of behavior, two important challenges have not been explored: i) Dealing with heterogeneous item transitions (both global and local perspectives). ii) Mitigating the issue of noise that arises from the incorporation of auxiliary behaviors. To address these issues, we propose a novel solution, Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation (GHTID). In particular, we view the transitions between behavior types of items as different relationships and propose two heterogeneous graphs. By considering the relationship between items under different behavioral types of transformations, we propose two heterogeneous graph convolution modules and explicitly learn heterogeneous item transitions. Moreover, we utilize two attention networks to integrate long-term and short-term interests associated with the target behavior to alleviate the noisy interference of auxiliary behaviors. Extensive experiments on four real-world datasets demonstrate that our method outperforms other state-of-the-art methods.
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Index Terms
- Global Heterogeneous Graph and Target Interest Denoising for Multi-behavior Sequential Recommendation
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March 2024
1246 pages
ISBN:9798400703713
DOI:10.1145/3616855
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- Andrés Muñoz Medina,
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Published: 04 March 2024
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WSDM '24: The 17th ACM International Conference on Web Search and Data Mining
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View all- Li QMa HJin WJi YLi Z(2024)Hypergraph-enhanced multi-interest learning for multi-behavior sequential recommendationExpert Systems with Applications10.1016/j.eswa.2024.124497255(124497)Online publication date: Dec-2024
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