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Semantic Search and Recommendation of e-Catalog Documents through Concept Network  

Lee, Jae-Won (서울대학교 전기컴퓨터 공학부)
Park, Sung-Chan (서울대학교 전기컴퓨터 공학부)
Lee, Sang-Keun (서울대학교 전기컴퓨터 공학부)
Park, Jae-Hui (서울대학교 전기컴퓨터 공학부)
Kim, Han-Joon (서울시립대학교 전자전기컴퓨터 공학부)
Lee, Sang-Goo (서울대학교 전기컴퓨터 공학부)
Publication Information
The Journal of Society for e-Business Studies / v.15, no.3, 2010 , pp. 131-145 More about this Journal
Abstract
Until now, popular paradigms to provide e-catalog documents that are adapted to users' needs are keyword search or collaborative filtering based recommendation. Since users' queries are too short to represent what users want, it is hard to provide the users with e-catalog documents that are adapted to their needs(i.e., queries and preferences). Although various techniques have beenproposed to overcome this problem, they are based on index term matching. A conventional Bayesian belief network-based approach represents the users' needs and e-catalog documents with their corresponding concepts. However, since the concepts are the index terms that are extracted from the e-catalog documents, it is hard to represent relationships between concepts. In our work, we extend the conventional Bayesian belief network based approach to represent users' needs and e-catalog documents with a concept network which is derived from the Web directory. By exploiting the concept network, it is possible to search conceptually relevant e-catalog documents although they do not contain the index terms of queries. Furthermore, by computing the conceptual similarity between users, we can exploit a semantic collaborative filtering technique for recommending e-catalog documents.
Keywords
Concept Network; Bayesian Belief Network; Semantic Search; Semantic Recommendation;
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