Content-only attention network for social recommendation

Computer Science and Information Systems

article

Bin Wu¹  Tao Zhang²  Yeh-Cheng Chen³ 

[1] School of Internet of Things Engineering, Jiangnan University;China Ship Scientific Research Center;Department of computer science, University of California

关键词: recommender system;    social network;    content-only multi-relational attention network;   

DOI  :  10.2298/CSIS220705012W

学科分类：土木及结构工程学

来源: Computer Science and Information Systems

【 摘 要 】

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.

【 授权许可】

CC BY-NC-ND