{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,27]],"date-time":"2025-12-27T10:13:47Z","timestamp":1766830427156},"reference-count":42,"publisher":"National Library of Serbia","issue":"2","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ComSIS","COMPUT SCI INF SYST","COMPUT SCI INFORM SY","COMPUTER SCI INFORM","COMSIS J"],"published-print":{"date-parts":[[2023]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.2298\/csis220705012w","type":"journal-article","created":{"date-parts":[[2023,3,1]],"date-time":"2023-03-01T08:28:09Z","timestamp":1677659289000},"page":"609-629","source":"Crossref","is-referenced-by-count":3,"title":["Content-only attention network for social recommendation"],"prefix":"10.2298","volume":"20","author":[{"given":"Bin","family":"Wu","sequence":"first","affiliation":[{"name":"School of Internet of Things Engineering, Jiangnan University, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Ship Scientific Research Center, Wuxi, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeh-Cheng","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of computer science, University of California, Davis, CA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1078","reference":[{"key":"ref1","unstructured":"Chen, X., et al.: DNNOff: Offloading DNN-based Intelligent IoT Applications in Mobile Edge Computing. 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