Extracting Core Users Based on Features of Users and Their Relationships in Recommender Systems

Li Kuang, Gaofeng Cao and Liang Chen
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Li Kuang: School of Software, Central South University, Changsha, China
Gaofeng Cao: School of Software, Central South University, Changsha, China
Liang Chen: School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China

International Journal of Web Services Research (IJWSR), 2017, vol. 14, issue 2, 1-23

Abstract: As an effective way to solve information overload, recommender system has drawn attention of scholars from various fields. However, existing works mainly focus on improving the accuracy of recommendation by designing new algorithms, while the different importance of individual users has not been well addressed. In this paper, the authors propose new approaches to identifying core users based on trust relationships and interest similarity between users, and the popular degree, trust influence and resource of individual users. First, the trust degree and interest similarity between all user pairs, as well as the three attributes of individuals are calculated. Second, a global core user set is constructed based on three strategies, which are frequency-based, rank-based, and fusion-sorting-based. Finally, the authors compare their proposed methods with other existing methods from accuracy, novelty, long-tail distribution and user degree distribution. Experiments show the effectiveness of the authors' core user extraction methods.

Date: 2017
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