Hypertext Information Retrieval for Short Queries

Keyword-based query model has been an immediate and eecient way to specify and retrieve related information on the Web. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given as in most cases. In order to clarify the ambiguity of the short queries given by users, we propose concept-based relevance feedback for Web information retrieval. This idea is to help users formulate their queries by having users give two to three times more feedback for traditional query methods. We apply clustering techniques to initial search results to provide concept-based browsing. We will show how clustering improves performance over conventional similarity ranking, and most importantly, the assistance of cluster-based representation can reduce the browsing labor for short queries.