Please use this identifier to cite or link to this item: https://doi.org/10.32604/cmc.2020.012593
Title: Recommender systems based on tensor decomposition
Authors: Sun, Zhoubao
Zhang, Xiaodong
Li, Haoyuan
Xiao, Yan 
Guo, Haifeng
Keywords: Recommender system
Social information
Tag regularization
Tensor decomposition
Issue Date: 1-Jan-2020
Publisher: Tech Science Press
Citation: Sun, 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. https://doi.org/10.32604/cmc.2020.012593
Rights: Attribution 4.0 International
Abstract: Recommender 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.
Source Title: Computers, Materials and Continua
URI: https://scholarbank.nus.edu.sg/handle/10635/232180
ISSN: 1546-2218
DOI: 10.32604/cmc.2020.012593
Rights: Attribution 4.0 International
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