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Machine-Learning Based on Relevance Feedback : A Powerful Engine to Enhance the Performance of SDI System

  • Journal of the Korean Society for Information Management
  • Abbr : JKOSIM
  • 2004, 21(4), pp.133~152
  • DOI : 10.3743/KOSIM.2004.21.4.133
  • Publisher : 한국정보관리학회
  • Research Area : Interdisciplinary Studies > Library and Information Science
  • Received : November 17, 2004
  • Accepted : December 13, 2004
  • Published : December 30, 2004

Younghee, Noh 1

1건국대학교

Accredited

ABSTRACT

As the Internet facilitates the rapid increase of information availability, the study on SDI service that provides users with relevant document in a timely manner has been developed. However, the practical use of this service has been low. This thesis aims at analyzing the reasons for this and developing relevance feedback based SDI system to improve the performance of the existing SDI system. Experimental systems that are developed for this study are SDI system based on users' minimum intervention feedback, SDI system based on perfect automation feedback, and SDI system based on users' maximum intervention feedback. The fourth system that utilizes the traditional SDI system is also studied to evaluate the level of performance improvement of the newly developed three types of SDI system. As a result of this study, SDI system based on users' maximum intervention feedback showed greatest performance improvement. The next performance improvement happened in order of SDI system based on perfect automation feedback, SDI system based on users' minimum intervention feedback, and the traditional SDI system. Feedback based systems showed greater performance improvement as they went through more feedback processes.

Citation status

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