Geodesic
Content-only attention Network for Social Recommendation
Computer Science and Information Systems, Tome 20 (2023) no. 2

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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.
Keywords: recommender system, social network, content-only multi-relational attention network 
@article{CSIS_2023_20_2_a4,
     author = {Bin Wu and Tao Zhang and Yeh-Cheng Chen},
     title = {Content-only attention {Network} for {Social} {Recommendation}},
     journal = {Computer Science and Information Systems},
     publisher = {mathdoc},
     volume = {20},
     number = {2},
     year = {2023},
     url = {http://geodesic.mathdoc.fr/item/CSIS_2023_20_2_a4/}
}
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Bin Wu; Tao Zhang; Yeh-Cheng Chen. Content-only attention Network for Social Recommendation. Computer Science and Information Systems, Tome 20 (2023) no. 2. http://geodesic.mathdoc.fr/item/CSIS_2023_20_2_a4/