Digital library retrieval model using subject classification table and user profile

Abstract

Existing library retrieval systems present users with massive results including irrelevant information. Thus, we propose SURM, a Retrieval Model using "Subject Classification Table" and "User Profile," to provide more relevant results. SURM uses Document Filtering technique for the classified data and Document Ranking technique for the non-classified data in the results from keyword-based retrieval system. We have performed experiment on the performance of filtering technique, updating method of user profile, and document ranking technique with the retrieval results. © Springer-Verlag Berlin Heidelberg 2004.