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Resisting spam in tagging system is very challenging. This paper presents Sitab, a novel spam-resistant tagging system which can significantly diminish spam in tag search results based on users' implicit tagging behavior. Sitab is trained to obtain the weights of the client's each type of implicit tagging behavior. For each tag search, Sitab ranks each resource in the results list according to its relevance degree which is calculated by the client's implicit tagging behavior with respect to that resource. Experimental results show that Sitab can effectively resist tag spam and work better than existing tag search schemes, especially in systems with large amount of spam tags.