Using Folksonomy for Building User Preference List

In this work, we use folksonomies for building user preference list (UPL) based on user's search history. A UPL is an indispensable source of knowledge which can be exploited by intelligent systems for query recommendation, personalized search, and web search result ranking etc. A UPL consist of list of concepts, and their weights, clustered together using agglomerative clustering by employing Google Similarity Distance. We show how to design and implement such a system in practice and visualize the UPL which aids in finding interesting relationships between terms and detect outliers, if any. The experiment reveals that UPL not only captures user interests but also its context and results are very promising.