{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T11:52:55Z","timestamp":1770033175157,"version":"3.49.0"},"reference-count":24,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2021,12,16]]},"abstract":"<jats:p>Tensors have been explored to share latent user-item relations and have been shown to be effective for recommendation. Tensors suffer from sparsity and cold start problems in real recommendation scenarios; therefore, researchers and engineers usually use matrix factorization to address these issues and improve the performance of recommender systems. In this paper, we propose matrix factorization completed multicontext data for tensor-enhanced algorithm a using matrix factorization combined with a multicontext data method for tensor-enhanced recommendation. To take advantage of existing user-item data, we add the context time and trust to enrich the interactive data via matrix factorization. In addition, Our approach is a high-dimensional tensor framework that further mines the latent relations from the user-item-trust-time tensor to improve recommendation performance. Through extensive experiments on real-world datasets, we demonstrated the superiority of our approach in predicting user preferences. This method is also shown to be able to maintain satisfactory performance even if user-item interactions are sparse.<\/jats:p>","DOI":"10.3233\/jifs-210641","type":"journal-article","created":{"date-parts":[[2021,10,15]],"date-time":"2021-10-15T18:32:53Z","timestamp":1634322773000},"page":"6727-6738","source":"Crossref","is-referenced-by-count":1,"title":["Matrix factorization completed multicontext data for tensor-enhanced recommendation"],"prefix":"10.1177","volume":"41","author":[{"given":"Shangju","family":"Deng","sequence":"first","affiliation":[{"name":"College of Information Sciences and Technology, School of Information Science and Engineering, Xinjiang University, Urumqi, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, China"}]},{"given":"Jiwei","family":"Qin","sequence":"additional","affiliation":[{"name":"College of Information Sciences and Technology, School of Information Science and Engineering, Xinjiang University, Urumqi, China"},{"name":"Key Laboratory of Signal Detection and Processing, Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, China"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-210641_ref1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.knosys.2013.03.012","article-title":"Recommender systems survey","volume":"46","author":"Bobadilla","year":"2013","journal-title":"Knowl Based Syst"},{"issue":"12","key":"10.3233\/JIFS-210641_ref4","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1145\/138859.138867","article-title":"Using collaborative fifiltering to weave an information tapestry","volume":"35","author":"Goldberg","year":"1992","journal-title":"Communications of the ACM"},{"key":"10.3233\/JIFS-210641_ref6","doi-asserted-by":"crossref","first-page":"1051","DOI":"10.1109\/TKDE.2011.18","article-title":"Mining web graphs for recommendations","volume":"24","author":"Ma","year":"2011","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.3233\/JIFS-210641_ref7","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.artint.2012.02.002","article-title":"Towards mobile intelligence: Learning from GPS history data for collaborative recommendation","volume":"184","author":"Zheng","year":"2012","journal-title":"Artif Intell"},{"key":"10.3233\/JIFS-210641_ref8","doi-asserted-by":"crossref","first-page":"100864","DOI":"10.1016\/j.jocs.2018.04.015","article-title":"R. 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