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.
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© 2004 Springer-Verlag Berlin Heidelberg
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Woo, SM., Yoo, CS. (2004). Digital Library Retrieval Model Using Subject Classification Table and User Profile. In: Chen, Z., Chen, H., Miao, Q., Fu, Y., Fox, E., Lim, Ep. (eds) Digital Libraries: International Collaboration and Cross-Fertilization. ICADL 2004. Lecture Notes in Computer Science, vol 3334. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30544-6_53
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DOI: https://doi.org/10.1007/978-3-540-30544-6_53
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-24030-3
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