Learning Ontology-Based User Profiles: A Semantic Approach to Personalized Web Search

Abstract

Abstract—Every user has a distinct background and a specific goal when searching for information on the Web. The goal of Web search personalization is to tailor search results to a particular user based on that user’s interests and preferences. Effective personalization of information access involves two important challenges: accurately identifying the user context, and organizing the information in such a way that matches the particular context. We present an approach to personalized search that involves modeling the user context as ontological profiles by assigning implicitly derived interest scores to existing concepts in a domain ontology. A spreading activation algorithm is used to maintain and incrementally update the interest scores based on the user’s ongoing behavior. Our experiments show that re-ranking the search results based on the interest scores and the semantic evidence captured in an ontological user profile enables an adaptive system to present the most relevant results to the user.