A Measuring Method for User Similarity based on Interest Topic

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 del.icio.us 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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