Reasonable tag-based collaborative filtering for social tagging systems

Abstract

In this paper, we 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, we 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, our system provides a higher level of relevant recommendations over other commonly used search and recommendation methods. We describe this system as well as the relevant user testing results and its implication towards use in online social tagging systems.