Intelligent Information Processing for User Modeling

This paper describes an architecture for modeling users with fuzzy conceptual graphs. The primary result is a method for finding similarity between fuzzy conceptual graphs. This allows the use of lazy learning techniques to make conjectures about users based on facts known about other users. These techniques have been used in user modeling research under the term collaborative filtering. Collaborative filtering focuses on the identification of users with similar characteristics to the active user and the use of the facts we know about them to recommend items. We show that by using our method, we explore user similarities that are not immediately obvious and thus will not be identified by similarity measures currently employed for user modeling.

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