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Generally, recommendation methods use users\u2019\n                    historical ratings on items to predict ratings on their unrated items to make\n                    recommendations. However, with the increase of the number of users and items,\n                    the degree of data sparsity increases, and the quality of recommendations\n                    decreases sharply. In order to solve the sparsity problem, other auxiliary\n                    information is combined to mine users\u2019 preferences for higher recommendation\n                    quality. Similar to rating data, review data also contain rich information about\n                    users\u2019 preferences on items. This paper proposes a novel recommendation model,\n                    which harnesses an adversarial learning among auto-encoders to improve\n                    recommendation quality by minimizing the gap of the rating and review relation\n                    between a user and an item. The empirical studies on real-world datasets show\n                    that the proposed method improves the recommendation performance.<\/jats:p>","DOI":"10.3233\/web-210449","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T15:00:38Z","timestamp":1617116438000},"page":"285-294","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Adversarial auto-encoder for rating prediction with ratings and                    reviews"],"prefix":"10.1177","volume":"18","author":[{"given":"Jin","family":"Yi","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology,\r                        Guizhou University, Guiyang, 550025,\r                        China.\r                        E-mails:\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiajin","family":"Huang","sequence":"additional","affiliation":[{"name":"International WIC Institute, Beijing University of Technology, Beijing, 100124,\r                        China. E-mail:\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jin","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology,\r                        Guizhou University, Guiyang, 550025,\r                        China.\r                        E-mails:\u00a0,\u00a0"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2005.99"},{"key":"e_1_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Y.Bao H.Fang and J.Zhang TopicMF: Simultaneously exploiting ratings and reviews for recommendation in: Proceedings of the 28th AAAI Conference on Artificial Intelligence 2014 pp. 2\u20138.","DOI":"10.1609\/aaai.v28i1.8715"},{"key":"e_1_3_2_4_2","doi-asserted-by":"crossref","unstructured":"R.Catherine and W.W.Cohen TransNets: Learning to transform for recommendation in: Proceedings of 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