Community-aware resource profiling for personalized search in folksonomy

In recent years, there is a fast proliferation of collaborative tagging (a.k.a. folksonomy) systems in Web 2.0 communities. With the increasingly large amount of data, how to assist users in searching their interested resources by utilizing these semantic tags becomes a crucial problem. 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. We propose, to tackle this problem in this paper, a community-aware approach to constructing resource profiles via social filtering. In order to discover user communities, three different strategies are devised and discussed. Moreover, we present a personalized search approach by combining a switching fusion method and a revised needs-relevance function, to optimize personalized resources ranking based on user preferences and user issued query. We conduct experiments on a collected real life dataset by comparing the performance of our proposed approach and baseline methods. The experimental results verify our observations and effectiveness of proposed method.