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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.


References: 16

    1. M. Callon, J. P. Courtial, and F. Laville, “Co-word Analysis as a Tool for Describing the Network of Interactions Between Basic and Technological Research: the Case of Polymer Chemsitry”, Scientometrics, Vol. 22 No. 1, pp. 155-205, 1991.
    2. Y. X. Chen, R. Santamaria, A. Butz, and R. Theron, “TagClusters: Semantic Aggregation of Collaborative Tags Beyond TagClouds”, in International Symposium on Smart Graphics, pp. 56-67, 2009.
    3. J. Gemmell, A. Shepitsen, B. Mobasher, and R. Burke, “Personalizing Navigation in Folksonomies Using Hierarchical Tag Clustering”, in International Conference on Data Warehousing and Knowledge Discovery, pp. 196-205, 2008.
    4. T. H. Haveliwala, “Topic-sensitive PageRank”, in International Conference on World Wide Web, pp. 517-526, 2002.
    5. C. C. Hung, Y. C. Huang, Y. J. Hsu, and K. C. Wu, “Tag-Based User Profiling for Social Media Recommendation”, AAAI Workshop - Technical Report, 2008.
    6. H. N. Kim, A. T. Ji, I. Ha, and G. S. Jo, “Collaborative Filtering Based on Collaborative Tagging for Enhancing the Quality of Recommendation”, Electronic Commerce Research & Applications, Vol. 9 No. 1, pp. 73-83, 2010.
    7. S. S. Kumar, and H. H. Inbarani, “Web 2.0 Social Bookmark Selection for Tag Clustering”, in International Conference on Pattern Recognition, Informatics and Mobile Engineering, pp. 510-516, 2013.
    8. H. Z. Li, X. G. Hu, Y. J. Lin, H. E. Wei, and J. H. Pan, “A Social Tag Clustering Method Based on Common Co-occurrence Group Similarity”, Frontiers of Information Technology & Electronic Engineering, Vol. 17 No. 2, pp. 122-134, 2016.
    9. H. Liang, Y. Xu, Y. Li, and R. Nayak, “Collaborative Filtering Recommender Systems Based on Popular Tags”, Adcs Proceedings of the Fourteenth Australasian Document Computing Symposium, 2009.
    10. C. Marlow, M. Naaman, D. Boyd, and M. Davis, “HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, to Read”, in Hypertext 2006, Proceedings of the ACM Conference on Hypertext and Hypermedia, Odense, Denmark, pp. 31-40, August 2006.
    11. W. Pan, S. Chen, and Z. Feng, “Automatic Clustering of Social Tag Using Community Detection”, Applied Mathematics & Information Sciences, Vol. 7 No. 2, pp. 675-681, 2013.
    12. X. Su, and T. M. Khoshgoftaar, “A Survey of Collaborative Filtering Techniques”, Advances in Artificial Intelligence, Vol. 2009 No. 12, pp. 4, 2009.
    13. K. H. L. Tso-Sutter, L. B. Marinho, and L. Schmidt-Thieme, “Tag-aware Recommender Systems by Fusion of Collaborative Filtering Algorithms”, Acm Symposium on Applied Computing, pp. 1995-1999, 2008.
    14. J. Vig, S. Sen, and J. Riedl, “Tagsplanations: Explaining Recommendations Using Tags”, in International Conference on Intelligent User Interfaces, pp. 47-56, 2009.
    15. S. Xu, S. Bao, B. Fei, Z. Su, and Y. Yu, “Exploring Folksonomy for Personalized Search”, in International ACM SIGIR Conference on Research and Development in Information Retrieval, Singapore, pp. 155-162, July 2008.
    16. “Hetrec2011-delicious-2k”, Available at, Last accessed on February 1, 2018.


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