{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T05:52:19Z","timestamp":1779342739730,"version":"3.51.4"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019,8]]},"abstract":"<jats:p>Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life. The majority of existing social recommendation methods unify user representation for the user-item interactions (item domain) and user-user connections (social domain). However, it may restrain user representation learning in each respective domain, since users behave and interact differently in the two domains, which makes their representations to be heterogeneous. In addition, most of traditional recommender systems can not efficiently optimize these objectives, since they utilize negative sampling technique which is unable to provide enough informative guidance towards the training during the optimization process. In this paper, to address the aforementioned challenges, we propose a novel deep adversarial social recommendation framework DASO. It adopts a bidirectional mapping method to transfer users' information between social domain and item domain using adversarial learning. Comprehensive experiments on two real-world datasets show the effectiveness of the proposed framework.<\/jats:p>","DOI":"10.24963\/ijcai.2019\/187","type":"proceedings-article","created":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:46:05Z","timestamp":1564299965000},"page":"1351-1357","source":"Crossref","is-referenced-by-count":49,"title":["Deep Adversarial Social Recommendation"],"prefix":"10.24963","author":[{"given":"Wenqi","family":"Fan","sequence":"first","affiliation":[{"name":"Department of Computer Science,City University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tyler","family":"Derr","sequence":"additional","affiliation":[{"name":"Data Science and Engineering Lab, Michigan State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Ma","sequence":"additional","affiliation":[{"name":"Data Science and Engineering Lab, Michigan State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Computer Science, City University of Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiliang","family":"Tang","sequence":"additional","affiliation":[{"name":"Data Science and Engineering Lab, Michigan State University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computing,The Hong Kong Polytechnic University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10584","event":{"name":"Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}","theme":"Artificial Intelligence","location":"Macao, China","acronym":"IJCAI-2019","number":"28","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2019,8,10]]},"end":{"date-parts":[[2019,8,16]]}},"container-title":["Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2019,7,28]],"date-time":"2019-07-28T07:47:27Z","timestamp":1564300047000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2019\/187"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2019,8]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2019\/187","relation":{},"subject":[],"published":{"date-parts":[[2019,8]]}}}