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Improving Recommendation Accuracy and Diversity via Multiple Social Factors and Social Circles
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Author(s): Yong Feng (Chongqing University, China), Heng Li (Chongqing University, China), Zhuo Chen (Chongqing University, China)and Baohua Qiang (Guilin University of Electronic Technology, China)
Copyright: 2019
Pages: 23
Source title:
Innovative Solutions and Applications of Web Services Technology
Source Author(s)/Editor(s): Liang-Jie Zhang (Kingdee International Software Group Co., Ltd., China)and Yishuang Ning (Tsinghua University, China)
DOI: 10.4018/978-1-5225-7268-8.ch006
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Abstract
Recommender systems have been widely employed to suggest personalized online information to simplify users' information discovery process. With the popularity of online social networks, analysis and mining of social factors and social circles have been utilized to support more effective recommendations, but have not been fully investigated. In this chapter, the authors propose a novel recommendation model with the consideration of more comprehensive social factors and topics. To further enhance recommendation accuracy, four social factors are simultaneously injected into the recommendation model based on probabilistic matrix factorization. Meanwhile, the authors explore several new methods to measure these social factors. Moreover, they infer explicit and implicit social circles to enhance the performance of recommendation diversity. Finally, the authors conduct a series of experiments on publicly available data. Experimental results show the proposed model achieves significantly improved performance over the existing models in which social information have not been fully considered.
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