A measuring method for user similarity based on interest topic

作     者：Bai, Yang Deng, Guishi Zhang, Liying Wang, Yi 

作者机构：School of System Engineering Eastern Liaoning University Dandong118003 China Institute of Systems Engineering Dalian University of Technology Dalian116024 China Department of Computer Science University of Texas at Dallas Richardson75080 United States School of Information Liaoning University Shenyang110036 China Information Center Liaoning University of Traditional Chinese Medicine Shenyang110032 China 

出 版 物：《International Journal of Performability Engineering》 (Int. J. Perform. Eng.)

年 卷 期：2018年第14卷第4期

页      面：691-698页

核心收录：

学科分类：1205[管理学-图书情报与档案管理] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This work was supported by the National Natural Science Foundation of China (Grant No. 71372083). The authors thank the anonymous reviewers for their helpful suggestions 

主　　题：Thesauri 

摘      要：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 highestP@ N and sorting accuracy compared with the traditional tag-based user similarity. © 2018 Totem Publisher, Inc. All rights reserved.