{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T00:28:51Z","timestamp":1777854531909,"version":"3.51.4"},"reference-count":28,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2016,6,1]],"date-time":"2016-06-01T00:00:00Z","timestamp":1464739200000},"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 Information Science"],"published-print":{"date-parts":[[2017,8]]},"abstract":"<jats:p>Collaborative filtering is a popular strategy in recommender systems area. This approach gathers users\u2019 ratings and then predicts what users will rate based on their similarity to other users. However, most of the collaborative filtering methods have faced problems such as sparseness and scalability. This paper presents a non-negative matrix factorisation method to alleviate these problems via decomposing rating matrix into user matrix and item matrix. This method tries to find two non-negative user matrix and item matrix whose product can well estimate the rating matrix. This approach proposes updated rules to learn the latent factors for factorising the rating matrix. The proposed method can estimate all the unknown ratings and its computational complexity is very low. Empirical studies on benchmark datasets show that the proposed method is more tolerant of the sparseness and scalability problems.<\/jats:p>","DOI":"10.1177\/0165551516654354","type":"journal-article","created":{"date-parts":[[2016,6,22]],"date-time":"2016-06-22T15:37:13Z","timestamp":1466609833000},"page":"567-579","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":26,"title":["Collaborative filtering using non-negative matrix factorisation"],"prefix":"10.1177","volume":"43","author":[{"given":"Mehdi Hosseinzadeh","family":"Aghdam","sequence":"first","affiliation":[{"name":"Payame Noor University, Iran"},{"name":"Iran University of Science and Technology, Iran"}]},{"given":"Morteza","family":"Analoui","sequence":"additional","affiliation":[{"name":"Iran University of Science and Technology, Iran"}]},{"given":"Peyman","family":"Kabiri","sequence":"additional","affiliation":[{"name":"Iran University of Science and Technology, Iran"}]}],"member":"179","published-online":{"date-parts":[[2016,6,1]]},"reference":[{"key":"bibr1-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1177\/0165551516628733"},{"key":"bibr2-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-85820-3_1"},{"key":"bibr3-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1177\/0165551512437517"},{"key":"bibr4-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1177\/0165551515592093"},{"key":"bibr5-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1177\/0165551514539870"},{"key":"bibr6-0165551516654354","first-page":"791","volume-title":"Proceedings of the ACM Symposium on Applied Computing","author":"Hofmann T","year":"2005"},{"key":"bibr7-0165551516654354","first-page":"688","volume":"99","author":"Hofmann T","year":"1999","journal-title":"IJCAI"},{"key":"bibr8-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1145\/1076034.1076056"},{"key":"bibr9-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2009.263"},{"key":"bibr10-0165551516654354","doi-asserted-by":"publisher","DOI":"10.1109\/ISTEL.2012.6483108"},{"issue":"5","key":"bibr11-0165551516654354","first-page":"2721","volume":"9","author":"Aghdam MH","year":"2015","journal-title":"Applied Mathematics & Information Sciences"},{"key":"bibr12-0165551516654354","volume-title":"Proceedings of the ACM Recommender Systems Conference (RecSys)","author":"Sindhwani V","year":"2009"},{"key":"bibr13-0165551516654354","doi-asserted-by":"crossref","unstructured":"Sarwar B, Karypis G, Konstan J, Riedl J. 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