{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T23:31:10Z","timestamp":1770593470392,"version":"3.49.0"},"reference-count":27,"publisher":"SAGE Publications","issue":"1","license":[{"start":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T00:00:00Z","timestamp":1478217600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,1,13]]},"abstract":"<jats:p>Nowadays, we are living in an information overload age. A tremendous amount of information has been produced on the Internet, how to find the interesting information is the main goal of recommendation system research. However, the most of current traditional recommendation algorithms (such as Collaborative filtering) are suffering from flowing difficulties: (i) The traditional recommendation system assume that users are independent and identically distributed; this assumption fails to consider the social relation and connection between users, which is not consistent with the social relations in our real world. (ii) Although there are some recommendation system research began to focus on the trust relationship between users, trust information is also very sparse. This leads to most of datasets only contains very little information about the user\u2019s relationship. In this paper, we propose an innovative method that integrated users\u2019 trust propagation and singular value decomposition into recommendation Algorithm to improve the quality of the recommendation effectively and efficiently. We performed our experiments on two real data sets respectively, the public domain Epinions.com and Filemtrust.com. The experimental results show that our method has a better outperform.<\/jats:p>","DOI":"10.3233\/jifs-16073","type":"journal-article","created":{"date-parts":[[2016,11,4]],"date-time":"2016-11-04T11:18:48Z","timestamp":1478258328000},"page":"807-816","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":8,"title":["A social recommendation method based on\u00a0trust propagation and singular value decomposition"],"prefix":"10.1177","volume":"32","author":[{"given":"Weijiang","family":"Li","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kumming, P.R. China"}]},{"given":"Jing","family":"Qi","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kumming, P.R. China"}]},{"given":"Zhengtao","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Yunnan, Kumming, P.R. China"}]},{"given":"Dongjun","family":"Li","sequence":"additional","affiliation":[{"name":"R&amp;D Department, Qingqi Peugeot motorcycle co. 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