Personalized Searching by Learning WordNet-based User Profiles

The amount of information available on the Web and in Digital Libraries is increasing over time. In this context, the role of user modeling and personalized information access is becoming crucial: Users need a personalized support in sifting through large amounts of retrieved information according to their interests. Information filtering and retrieval systems relying on this idea adapt their behavior to individual users by learning their preferences during the interaction in order to construct a profile of the user that can be later exploited in the search process. We propose a novel technique to learn user profiles which exploits word sense disambiguation based on the WordNet lexical database, in an attempt to produce semantic user profiles that might discover topics semantically closer to the user interests. Semantic profiles are used in the definition of a retrieval model that turns the traditional document-query search paradigm into a novel document-query-profile paradigm. As an example of this paradigm, we present an extension of the vector space model in which profiles are used to modify the ranking of search results obtained in response to a query, hopefully putting personally relevant items on the top of the result list. Experimental results in a movie retrieval scenario indicate that the proposed model to personalize Web search is effective