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In this paper, the authors present a tag-based collaborative filtering recommendation method for use with recently popular online social tagging systems. Combining the information provided by tagging systems with the effective recommendation abilities given by collaborative filtering, they provide a website recommendation system which provides relevant, credible recommendations that match the user's changing interests as well as the user's bookmarking profile. Based upon user testing, the system provides a higher level of relevant recommendations over other commonly used search and recommendation methods.
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