With the popularity of the collaborative tagging communities in Web 2.0, collaborative tagging (a.k.a folksonomy) becomes an important way to index and organize user interested resources. Collaborative tagging systems provide an environment for users to annotate resources, and most users give annotations according to their perspectives or feelings. However, users may have different perspectives or feelings on resources, e.g., some of them may share similar perspectives yet have a conflict with others. Thus, modeling the profile of a resource based on tags given by all users who have annotated the resource is neither suitable nor reasonable. To address this problem, in this paper, we propose a social filtering approach to resource profiling for personalized search. By utilizing the proposed resource profiles, we devise a personalized search approach to optimal resource ranking based on user preference. We conduct experiments on our FMRS data set by comparing the performance of the proposed approach and baseline methods, and the experimental results verify our observations
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