Retrieval Model using Subject Classification Table, User Profile, and LSI 


Vol. 12,  No. 5, pp. 789-796, Oct.  2005
10.3745/KIPSTD.2005.12.5.789


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  Abstract

Because existing information retrieval systems, in particular library retrieval systems, use ´exact keyword matching´ with user´s query, they present user with massive results including irrelevant information. So, a user spends extra effort and time to get the relevant information from the results. Thus, this paper will propose SULRM, a Retrieval Model using Subject Classification Table, User profile, and LSI(Latent Semantic Indexing), to provide more relevant results. SULRM uses document filtering technique for classified data and document ranking technique for non-classified data in the results of keyword-based retrieval. Filtering technique uses Subject Classification Table, and ranking technique uses user profile and LSI. And, we have performed experiments on the performance of filtering technique, user profile updating method, and document ranking technique using the results of information retrieval system of our university´ digital library system. In case that many documents are retrieved, proposed techniques are able to provide user with filtered data and ranked data according to user´s subject and preference.

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[IEEE Style]

S. M. Woo, "Retrieval Model using Subject Classification Table, User Profile, and LSI," The KIPS Transactions:PartD, vol. 12, no. 5, pp. 789-796, 2005. DOI: 10.3745/KIPSTD.2005.12.5.789.

[ACM Style]

Seon Mi Woo. 2005. Retrieval Model using Subject Classification Table, User Profile, and LSI. The KIPS Transactions:PartD, 12, 5, (2005), 789-796. DOI: 10.3745/KIPSTD.2005.12.5.789.

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