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SIGA: social influence modeling integrating graph autoencoder for rating prediction

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Abstract

With the revival of social networks, many studies try to integrate social relations of users to improve the accuracy of rating prediction. However, most existing methods cannot accurately reflect how social relations affect user preferences. The main reason is that these methods only investigate the directly or indirectly reachable nodes in social networks, while ignoring global transitivity of influence. Actually, for a particular user, his preference will not only affect his neighbors but also some people he does not know, especially when the person is an opinion leader. In this paper, we propose a social influence model, SIGA, which integrates graph autoencoder and is used for the rating prediction task. First, we establish a new method to quantify the social influence of users from the perspective of information dissemination in social networks. Second, we employ graph autoencoder (GAE) to model the interaction between users and items from the view of message passing on bipartite graph, which can obtain high-quality representations of users and items. By building a hybrid architecture of social modeling and GAE, it is expected to be endowed with both benefits from them. In addition, our model is interpretable at both the structural level and attribute level. Extensive experimental results on four real-world datasets have shown the effectiveness and generalization of the proposed model.

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Acknowledgements

This work is supported by Tianjin “Project + Team” Key Training Project under Grant No. XC202022.

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Authors and Affiliations

  1. Engineering Research Center of Learning-Based Intelligent System, Ministry of Education, Tianjin University of Technology, Tianjin, 300384, China

    Jinxin Liu, Yingyuan Xiao & Wenguang Zheng

  2. Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, 300384, China

    Jinxin Liu, Yingyuan Xiao & Wenguang Zheng

  3. College of Information and Electrical Engineering, Asia University, Taichung, 41354, Taiwan

    Ching-Hsien Hsu

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  1. Jinxin Liu
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  2. Yingyuan Xiao
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  4. Ching-Hsien Hsu
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Correspondence to Yingyuan Xiao or Wenguang Zheng.

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Liu, J., Xiao, Y., Zheng, W. et al. SIGA: social influence modeling integrating graph autoencoder for rating prediction. Appl Intell 53, 6432–6447 (2023). https://doi.org/10.1007/s10489-022-03748-1

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