research-article

BA-GNN: Behavior-aware graph neural network for session-based recommendation

Authors:

Yongquan Liang, Qiuyu Song, Zhongying Zhao, Hui Zhou, Maoguo GongAuthors Info & Claims

Frontiers of Computer Science, Volume 17, Issue 6

https://doi.org/10.1007/s11704-022-2324-x

Published: 14 February 2023 Publication History

Abstract

Session-based recommendation is a popular research topic that aims to predict users’ next possible interactive item by exploiting anonymous sessions. The existing studies mainly focus on making predictions by considering users’ single interactive behavior. Some recent efforts have been made to exploit multiple interactive behaviors, but they generally ignore the influences of different interactive behaviors and the noise in interactive sequences. To address these problems, we propose a behavior-aware graph neural network for session-based recommendation. First, different interactive sequences are modeled as directed graphs. Thus, the item representations are learned via graph neural networks. Then, a sparse self-attention module is designed to remove the noise in behavior sequences. Finally, the representations of different behavior sequences are aggregated with the gating mechanism to obtain the session representations. Experimental results on two public datasets show that our proposed method outperforms all competitive baselines. The source code is available at the website of GitHub.

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BA-GNN: Behavior-aware graph neural network for session-based recommendation

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Published In

cover image Frontiers of Computer Science: Selected Publications from Chinese Universities

Frontiers of Computer Science: Selected Publications from Chinese Universities Volume 17, Issue 6

Dec 2023

184 pages

ISSN:2095-2228

EISSN:2095-2236

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© Higher Education Press 2023.

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 14 February 2023

Accepted: 28 September 2022

Received: 29 May 2022

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Cited By

Sun YJi YZhu HZhuang FHe QXiong H(2025)Market-aware Long-term Job Skill Recommendation with Explainable Deep Reinforcement LearningACM Transactions on Information Systems10.1145/370499843:2(1-35)Online publication date: 22-Jan-2025
https://dl.acm.org/doi/10.1145/3704998
Yue LLiu QLiu YGao WYao F(2025)Learning from shortcut: a shortcut-guided approach for explainable graph learningFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-024-40452-419:8Online publication date: 1-Aug-2025
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Wu JLiu XCheng DOuyang YWu XZheng YLarson K(2024)Safeguarding fraud detection from attacksProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/830(7500-7508)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/830
Li FWang XCheng DZhang WZhang YLin XLarson K(2024)Hypergraph self-supervised learning with sampling-efficient signalsProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/486(4398-4406)Online publication date: 3-Aug-2024
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Xu QYang JZhang FChen ZGuan JChen KFan JShen YYang KZhang YDu X(2024)Improving Graph Compression for Efficient Resource-Constrained Graph AnalyticsProceedings of the VLDB Endowment10.14778/3665844.366585217:9(2212-2226)Online publication date: 6-Aug-2024
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Zhu XLin FZhao ZXu TZhao XYin ZLi XChen E(2024)Multi-Behavior Recommendation with Personalized Directed Acyclic Behavior GraphsACM Transactions on Information Systems10.1145/369641743:1(1-30)Online publication date: 9-Dec-2024
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Cheng MLiu QZhang WLiu ZZhao HChen E(2023)A general tail item representation enhancement framework for sequential recommendationFrontiers of Computer Science: Selected Publications from Chinese Universities10.1007/s11704-023-3112-y18:6Online publication date: 28-Dec-2023
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