Inferring User’s Information Context from User Profiles and Concept Hierarchies

The critical elements that make up a user’s information context include the user profiles that reveal long-term interests and trends, the short-term information need as might be expressed in a query, and the semantic knowledge about the domain being investigated. The next generation of intelligent information agents, that can seamlessly integrate these elements into a single framework, are enabled to effectively locate and provide the most appropriate results for users’ information needs. In this paper we present one such framework for contextualized information access. We model the problem in the context of our client-side Web agent ARCH (Adaptive Retrieval based on Concept Hierarchies). In ARCH, the user profiles are generated using an unsupervised document clustering technique. These profiles, in turn, are used to automatically learn the semantic context of user’s information need from a domain-specific concept hierarchy. Our experimental results show that implicit measures of user interests, combined with the semantic knowledge embedded in a concept hierarchy, can be used effectively to infer the user context and improve the results of information retrieval.

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