@Article{cmc.2020.012593,
AUTHOR = {Zhoubao Sun, Xiaodong Zhang, Haoyuan Li, Yan Xiao, Haifeng Guo},
TITLE = {Recommender Systems Based on Tensor Decomposition},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {621--630},
URL = {http://www.techscience.com/cmc/v66n1/40469},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.012593}
}