Content-only attention Network for Social Recommendation

Bin Wu1, Tao Zhang2 and Yeh-Cheng Chen3

  1. School of Internet of Things Engineering, Jiangnan University,
    Wuxi, 214122, China
  2. China Ship Scientific Research Center,
    Wuxi 214122, China
  3. Department of computer science, University of California,
    Davis, CA, USA


With the rapid growth of social Internet technology, social recommender has emerged as a major research hotspot in the recommendation systems. However, traditional graph neural networks does not consider the impact of noise generated by long-distance social relations on recommendation performance. In this work, a content-only multi-relational attention network (CMAN) is proposed for social recommendation. The proposed model owns the following advantages: (i) the comprehensive trust based on the historical interaction records of users and items are integrated into the recursive social dynamic modeling to obtain the comprehensive trust of different users; (ii) social trust information is captured based on the attention network mechanism, so as to solve the problem of weight distribution in the same level domain; (iii) two levels of attention mechanisms are merged into a unified framework to enhance each other. Experiments conducted on two representative datasets demonstrate that the proposed algorithm outperforms previous methods substantially.

Key words

recommender system, social network, content-only multi-relational attention network

Digital Object Identifier (DOI)

Publication information

Volume 20, Issue 2 (April 2023)
Special Issue on Machine Learning-based Decision Support Systems in IoT systems
Year of Publication: 2023
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

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How to cite

Wu, B., Zhang, T., Chen, Y.: Content-only attention Network for Social Recommendation. Computer Science and Information Systems, Vol. 20, No. 2, 609–629. (2023),