Adaptive and incremental query expansion for cluster-based browsing

In this paper, we propose a new method of information retrieval which combines adaptive and incremental query expansion with cluster-based browsing. The proposed method attempts to accurately learn users' interests from their relevance judgments on clustered search results instead of individual documents, reducing users' loads for the judgments. The use of adaptive relevance feedback leads to the capability for tracking vague or dynamically shifting goals of users. Incrementally expanded and refined queries can be used in re-searching to improve the retrieval effectiveness. We apply the proposed method to the information retrieval on the World Wide Web and demonstrate its effectiveness through basic experiments.