Live-Updating Website Recommendations Using Reasonable Tag-based Collaborative Filtering
Reyn Nakamoto, Shinsuke Nakajima, Jun Miyazaki, Shunsuke Uemura, Hirokazu Kato · Tokyo Tech Research Repository (Tokyo Institute of Technology) · 2008
In this paper, we present a tag-based collaborative filtering recommendation method for use with re- cently popular online social tagging systems. Combining the information provided by tagging systems with the effective recommendation abilities given by collaborative filtering, we present a website recommendation system which provides live-updating personalized recommendations that update to 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 recommendation 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.