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A Measuring Method for User Similarity based on Interest Topic

Volume 14, Number 4, April 2018, pp. 691-698
DOI: 10.23940/ijpe.18.04.p12.691698

Yang Baia,b,c, Guishi Dengb, Liying Zhangd,e, and Yi Wanga

aSchool of System Engineering, Eastern Liaoning University, Dandong, 118003, China
bInstitute of Systems Engineering, Dalian University of Technology, Dalian, 116024, China
cDepartment of Computer Science, The University of Texas at Dallas, Richardson, 75080, USA
dSchool of Information, Liaoning University, Shenyang, 110036, China
eInformation Center, Liaoning University of Traditional Chinese Medicine, Shenyang, 110032, China

(Submitted on December 22, 2017; Revised on January 30, 2018; Accepted on March 8, 2018)


A key problem in user relationship analysis is the identification and representation of user interest. The basis to tackle this issue is user similarity measures. In social tagging system, users collaboratively create and manage tags to annotate and categorize content for searching and recommending. Due to the contribution to reflect users’ opinions and interests, tags are metadata for user similarity measures. However, there are some issues about it such as data sparseness, the user none-distinguished interest areas and relatively little consider about user influence. This article argues a similarity measure method that based on user’s interest topic division. First, we construct tag clustering and divide the user community according to user interest areas. Second, we improve user similarity measurement model using social network analysis (SNA) and PageRank. Finally, the validity of the improved method about user similarity calculation is verified using data set. Experimental results show that the improved method gets the highest P@N and sorting accuracy compared with the traditional tag-based user similarity.


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