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

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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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Acknowledgements

This research was supported by the National Natural Science Foundation of China (Grant Nos. 62072288, 61702306, 61433012), the Taishan Scholar Program of Shandong Province, the Natural Science Foundation of Shandong Province (ZR2018BF013, ZR2022MF268), and the Open Project from CAS Key Lab of Network Data Science and Technology (CASNDST202007).

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Authors and Affiliations

  1. School of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, China

    Yongquan Liang, Qiuyu Song, Zhongying Zhao & Maoguo Gong

  2. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Hui Zhou & Maoguo Gong

Authors
  1. Yongquan Liang
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  2. Qiuyu Song
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  3. Zhongying Zhao
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  4. Hui Zhou
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  5. Maoguo Gong
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Corresponding authors

Correspondence to Zhongying Zhao or Maoguo Gong.

Additional information

Yongquan Liang received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 1999. He is currently a professor in School of Computer Science and Engineering, Shandong University of Science and Technology, China. His research interests include social network analysis, expert system and data mining.

Qiuyu Song is pursuing the Master degree in School of Computer Science and Engineering, Shandong University of Science and Technology, China. Her research interests include sessionbased recommendation and graph neural networks.

Zhongying Zhao (Corresponding author) received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2012. She is currently an associate professor in School of Computer Science and Engineering, Shandong University of Science and Technology, China. Her research interests include social network analysis, graph neural networks and data mining. She has published more than 40 papers in international journals and conferences, such as IEEE Transactions on Network Science and Engineering, ACM Transactions on Multimedia Computing, Communications, and Applications.

Hui Zhou received the MS degree in computer science from Shandong University of Science and Technology, China in 2020. She is currently pursuing PhD degree in pattern recognition and intelligent systems from School of Electronic Engineering, the Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China, Xidian University, China. Her research interests include graph neural networks, network embedding, and artificial intelligence.

Maoguo Gong (Corresponding author) received the BSc degree in electronic engineering and the PhD degree in electronic science and technology from Xidian University, China in 2003 and 2009, respectively. Since 2006, he has been a Teacher with Xidian University, where he was promoted as an Associate Professor and as a Full Professor in 2008 and 2010, with exceptive admission. His research interests are in the areas of computational intelligence with applications to optimization, learning, data mining, and image understanding. Prof. Gong is currently the Vice Chair of the IEEE Computational Intelligence Society Task Force on Memetic Computing. He is an Executive Committee Member of the Chinese Association for Artificial Intelligence and a Senior Member of the Chinese Computer Federation.

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Liang, Y., Song, Q., Zhao, Z. et al. BA-GNN: Behavior-aware graph neural network for session-based recommendation. Front. Comput. Sci. 17, 176613 (2023). https://doi.org/10.1007/s11704-022-2324-x

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

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