Exploration of Website Recommendation Using Reasonable Tag-Based Collaborative Filtering

Reyn Nakamoto (0651149)

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 present a recommendation method which takes into consideration not only the user's preferences, but additionally tries to understand why a user liked something. Using this method, we created a website recommendation system which provides live-updating personalized recommendations that updates according to the user's changing interests. 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.