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BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation

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Abstract

Session-based recommendation (SBR) and multi-behavior recommendation (MBR) are both important problems and have attracted the attention of many researchers and practitioners. Different from SBR that solely uses one single type of behavior sequences and MBR that neglects sequential dynamics, heterogeneous SBR (HSBR) that exploits different types of behavioral information (e.g., examinations like clicks or browses, purchases, adds-to-carts and adds-to-favorites) in sequences is more consistent with real-world recommendation scenarios, but it is rarely studied. Early efforts towards HSBR focus on distinguishing different types of behaviors or exploiting homogeneous behavior transitions in a sequence with the same type of behaviors. However, all the existing solutions for HSBR do not exploit the rich heterogeneous behavior transitions in an explicit way and thus may fail to capture the semantic relations between different types of behaviors. However, all the existing solutions for HSBR do not model the rich heterogeneous behavior transitions in the form of graphs and thus may fail to capture the semantic relations between different types of behaviors. The limitation hinders the development of HSBR and results in unsatisfactory performance. As a response, we propose a novel behavior-aware graph neural network (BGNN) for HSBR. Our BGNN adopts a dual-channel learning strategy for differentiated modeling of two different types of behavior sequences in a session. Moreover, our BGNN integrates the information of both homogeneous behavior transitions and heterogeneous behavior transitions in a unified way. We then conduct extensive empirical studies on three real-world datasets, and find that our BGNN outperforms the best baseline by 21.87%, 18.49%, and 37.16% on average correspondingly. A series of further experiments and visualization studies demonstrate the rationality and effectiveness of our BGNN. An exploratory study on extending our BGNN to handle more than two types of behaviors show that our BGNN can easily and effectively be extended to multi-behavior scenarios.

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Acknowledgements

We thank the support of the National Natural Science Foundation of China (Grant Nos. 62172283 and 61836005).

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Authors and Affiliations

  1. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, 518060, China

    Jinwei Luo, Mingkai He, Weike Pan & Zhong Ming

  2. National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen, 518060, China

    Jinwei Luo, Mingkai He, Weike Pan & Zhong Ming

Authors
  1. Jinwei Luo
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  2. Mingkai He
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  3. Weike Pan
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  4. Zhong Ming
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Corresponding author

Correspondence to Weike Pan.

Additional information

Jinwei Luo received the BS degree in College of Mechatronics and Control Engineering from Shenzhen University, China in 2020. He is currently pursuing the MS degree with the College of Computer Science and Software Engineering, Shenzhen University, China. His research interests include recommender systems and deep learning. He has published papers in JOCA, TKDE, TIST, KDD, and CIKM.

Mingkai He received the MS degree in Software Engineering from Shenzhen University, China in 2022. He is currently an Engineer at Baidu, Shenzhen, China. His research interests include recommender systems and deep learning. He has published papers in FCS, TIST, INS, KDD, CIKM, and RecSys.

Weike Pan received the PhD degree in Computer Science and Engineering from the Hong Kong University of Science and Technology, China in 2012. He is currently an associate professor with the College of Computer Science and Software Engineering, Shenzhen University, China. His research interests include transfer learning, federated learning, recommender systems and machine learning. He has published research papers in AIJ, TBD, TIIS, TIST, TKDE, TOIS, AAAI, CIKM, IJCAI, RecSys, SDM, SIGIR, WSDM, etc.

Zhong Ming received the PhD degree in Computer Science and Technology from the Sun Yat-Sen University, China in 2003. He is currently a professor with the College of Computer Science and Software Engineering, Shenzhen University, China. His research interests include software engineering and artificial intelligence. He has published more than 200 refereed international conference and journal papers (including 40+ ACM/IEEE Transactions papers). He was the recipient of the ACM TiiS 2016 Best Paper Award and some other best paper awards.

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Luo, J., He, M., Pan, W. et al. BGNN: Behavior-aware graph neural network for heterogeneous session-based recommendation. Front. Comput. Sci. 17, 175336 (2023). https://doi.org/10.1007/s11704-022-2100-y

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