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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
   wubin@jiangnan.edu.cn
2. China Ship Scientific Research Center,
   Wuxi 214122, China
   taozhang@jiangnan.edu.cn
3. Department of computer science, University of California,
   Davis, CA, USA
   ycch@ucdavis.edu

Abstract

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)

https://doi.org/10.2298/CSIS220705012W

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), https://doi.org/10.2298/CSIS220705012W