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A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation

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Abstract

Session-based recommendation systems aim to predict the next user interaction based on the items with which the user interacts in the current session. Currently, graph neural network-based models have been widely used and proven more effective than others. However, these session-based models mainly focus on the user-item and item-item relations in historical sessions while ignoring information shared by similar users. To address the above issues, a new graph-based representation, User-item Group Graph, which considers not only user-item and item-item but also user-user relations, is developed to take advantage of natural sequential relations shared by similar users. A new personalized session-based recommendation model is developed based on this representation. It first generates groups according to user-related historical item sequences and then uses a user group preference recognition module to capture and balance between group-item preferences and user-item preferences. Comparison experiments show that the proposed model outperforms other state-of-art models when similar users are effectively grouped. This indicates that grouping similar users can help find deep preferences shared by users from the same group and is instructive in finding the most appropriate next item for the current user.

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Acknowledgements

The work is supported by the National Natural Science Foundation of China (No. 41871286) and the 1331 Engineering Project of Shanxi Province, China.

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Authors and Affiliations

  1. Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education, School of Computer and Information Technology, Shanxi University, Taiyuan, 030006, China

    Hui Wang, Hexiang Bai, Jun Huo & Minhu Yang

Authors
  1. Hui Wang

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  2. Hexiang Bai

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  3. Jun Huo

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  4. Minhu Yang

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Corresponding author

Correspondence toHexiang Bai.

Editor information

Editors and Affiliations

  1. Central South University, Changsha, China

    Biao Luo

  2. Chinese Academy of Sciences, Beijing, China

    Long Cheng

  3. Zhejiang University, Hangzhou, China

    Zheng-Guang Wu

  4. Guangdong University of Technology, Guangzhou, China

    Hongyi Li

  5. UNSW Sydney, Sydney, NSW, Australia

    Chaojie Li

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Wang, H., Bai, H., Huo, J., Yang, M. (2024). A Graph Convolution Neural Network for User-Group Aided Personalized Session-Based Recommendation. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_26

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