Textual information retrieval with user profiles using fuzzy clustering and inferencing

Authors:
Donald H. Kraft;Jianhua Chen;Maria J. Martin-Bautista;Maria-Amparo Vila
Affiliations:
Department of Computer Science, Louisiana State University, Baton Rouge, LA;Department of Computer Science, Louisiana State University, Baton Rouge, LA;Department of Computer Science and Artificial Intelligence University of Granada, Avda. Andalucia 38, Granada 18071, Spain;Department of Computer Science and Artificial Intelligence University of Granada, Avda. Andalucia 38, Granada 18071, Spain
Venue:
Intelligent exploration of the web
Year:
2003

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Abstract

We present a fuzzy-logic based approach to construction and use of user profiles in web textual information retrieval. A classical user profile is a collection of terms extracted from the set of documents for a specific user or a group of users. We use a fuzzy representation for user profiles where each term in a profile is associated with a fuzzy membership value. The construction of user profiles is performed by a combination of fuzzy clustering and fuzzy inferencing, a new approach developed recently. We apply fuzzy clustering methods (such as fuzzy c-means and fuzzy hierarchical clustering) to cluster documents relevant to a user. From the cluster centers (prototypes), a user profile is constructed which indicates the user's general preference of various terms. Fuzzy logic rules are also extracted from the cluster centers or from the user profiles. The fuzzy rules specify the semantic correlation among query terms. The user profiles and the fuzzy rules are subsequently used to expand user queries for better retrieval performance. Additional non-topical information about the user can be added to personalize the retrieval process. Moreover, fuzzy clustering can be applied to profiles of many users to extract knowledge about different user groups. The extracted knowledge is potentially useful for personalized marketing on the web.