Spear: spamming-resistant expertise analysis and ranking in
                    collaborative tagging systems

Spear: spamming-resistant expertise analysis and ranking in collaborative tagging systems

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Article ID: iaor201112698
Volume: 27
Issue: 3
Start Page Number: 458
End Page Number: 488
Publication Date: Aug 2011
Journal: Computational Intelligence
Authors: , , , ,
Keywords: collaboration systems, ranking
Abstract:

In this article, we discuss the notions of experts and expertise in resource discovery in the context of collaborative tagging systems. We propose that the level of expertise of a user with respect to a particular topic is mainly determined by two factors. First, an expert should possess a high-quality collection of resources, while the quality of a Web resource in turn depends on the expertise of the users who have assigned tags to it, forming a mutual reinforcement relationship. Second, an expert should be one who tends to identify interesting or useful resources before other users discover them, thus bringing these resources to the attention of the community of users. We propose a graph-based algorithm,SPEAR (spamming-resistant expertise analysis and ranking), which implements the above ideas for ranking users in a folksonomy. Our experiments show that our assumptions on expertise in resource discovery, and SPEAR as an implementation of these ideas, allow us to promote experts and demote spammers at the same time, with performance significantly better than the original hypertext-induced topic search algorithm and simple statistical measures currently used in most collaborative tagging systems.

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