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dc.titleRecommender systems based on tensor decomposition
dc.contributor.authorSun, Zhoubao
dc.contributor.authorZhang, Xiaodong
dc.contributor.authorLi, Haoyuan
dc.contributor.authorXiao, Yan
dc.contributor.authorGuo, Haifeng
dc.identifier.citationSun, Zhoubao, Zhang, Xiaodong, Li, Haoyuan, Xiao, Yan, Guo, Haifeng (2020-01-01). Recommender systems based on tensor decomposition. Computers, Materials and Continua 66 (1) : 621-630. ScholarBank@NUS Repository.
dc.description.abstractRecommender system is an effective tool to solve the problems of information overload. The traditional recommender systems, especially the collaborative filtering ones, only consider the two factors of users and items. While social networks contain abundant social information, such as tags, places and times. Researches show that the social information has a great impact on recommendation results. Tags not only describe the characteristics of items, but also reflect the interests and characteristics of users. Since the traditional recommender systems cannot parse multi-dimensional information, in this paper, a tensor decomposition model based on tag regularization is proposed which incorporates social information to benefit recommender systems. The original Singular Value Decomposition (SVD) model is optimized by mining the co-occurrence and mutual exclusion of tags, and their features are constrained by the relationship between tags. Experiments on real dataset show that the proposed algorithm achieves superior performance to existing algorithms. © 2020 Tech Science Press. All rights reserved.
dc.publisherTech Science Press
dc.rightsAttribution 4.0 International
dc.sourceScopus OA2021
dc.subjectRecommender system
dc.subjectSocial information
dc.subjectTag regularization
dc.subjectTensor decomposition
dc.contributor.departmentDEPARTMENT OF COMPUTER SCIENCE
dc.description.sourcetitleComputers, Materials and Continua
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