An Information Theoretic Approach for Ontology-based Interest Matching

Designing a general algorithm for interest matching is a major challenge in building online community and agent-based communication networks. This paper presents an information theoretic concept-matching approach to measure degrees of similarity among users. Kullback-Leiber distance is used as a measure of similarity on users represented by concept hierarchy. Preliminary sensitivity analysis shows that KL distance has more interesting properties and is more noise tolerant than keyword-overlap approaches. A multi-agent system has also been built to deploy the interest-matching algorithm.