Journal of KIISE:Software and Applications (한국정보과학회논문지:소프트웨어및응용)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Personalized Topic map Ranking Algorithm using the User Profile

사용자 프로파일을 이용한 개인화된 토픽맵 랭킹 알고리즘

  • 박정우 (국방대학교 전산정보학과) ;
  • 이상훈 (국방대학교 전산정보학과)
  • Published : 2008.08.15
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Abstract

Topic map typically provide information to user through the selection of topics, that is using only topic, association, occurrence on the first topicmap which is made by domain expert without regard to individual interests or context, for the purpose of supplementation for the weakness which is providing personalized topic map information, personalization has been studied for supporting user preference through preseting of customize, filtering, scope, etc in topic map. Nevertheless, personalization in current topicmap is not enough to user so far. In this paper, we propose a design of PTRS(personalized topicmap ranking system) & algorithm, using both user profile(click through data) and basic element of topic map(topic, association) on knowledge layer in specific domain topicmap, therefore User has strong point that is improvement of personal facilities to user through representation of ranked topicmap information in consideration of user preference using PTRS.

토픽맵에서 사용자의 토픽 선택에 따라 제공되는 정보는 개별 사용자의 관심과 배경지식이 고려되지 않고 최초 도메인 전문가에 의해 구축된 토픽맵 상의 토픽(Topic)과 연관되는 관계(Association), 자원(Occurrence)만을 이용하여 사용자에게 토픽맵 정보를 제공하고 있다. 이에 토픽맵은 개인화된 정보제공 측면의 단점을 보완하고자 개별 사용자를 위한 개인화 기능으로 개인 선호항목 설정, 필터링(Filtering), 범위제한(Scope) 등 사용자가 직접 관심정보를 사전에 설정하는 기능을 제공하고 있으나 토픽맵 사용자를 위한 개인화 측면에서 만족스럽지 못하다. 따라서 본 논문에서는 특정 도메인 토픽맵에서 사용자가 원하는 개인화된 정보를 제공하기 위해 사용자 클릭정보 수집을 통한 프로파일 정보와 이를 이용한 토픽 선호도 백터(Topic Preference Vector), 토픽맵 지식층의 기본요소인 토픽(Topic)과 관계(Association)를 이용한 개인화된 토픽맵 랭킹 알고리즘(PTR)을 제안한다. 사용자는 PTR 알고리즘을 이용하여 개인 선호도가 고려되어 랭킹된 토픽맵 정보를 제공받을 수 있게 됨으로써 개인화된 정보 제공 측면에서의 성능 향상을 가져올 수 있는 장점을 가진다.

Keywords

References

  1. Dandan Wang, Darina Dicheva, Christo Dichev, Jerry Akouala, "Retrieving information in topic maps: the case of TM4L," Proceedings of the 45th annual southeast regional conference, pp.88-93, 2007
  2. 한국전자거래진흥원, TopicMaps 응용 표준 및 활용 가이드라인 개발, 2003
  3. Steve Pepper, "The TAO of Topic Maps," http:// www.ontopia.net
  4. http://www.frotoma.com
  5. Liu F, Yu C and Meng W. "Personalized Web search by mapping user queries to categories," In Proceedings of CIKM'02, pp.558-565, 2002
  6. Yabo Xu, Ke Wang, Benyu Zhang, Zheng Chen, "Privacy-enhancing personalized web search," Proceedings of the 16th international conference on World Wide Web, pp.591-600. 2007
  7. Google personalized search, http://www.google.com /psearch
  8. Zhongming Ma, Gautam Pant, Olivia R. Liu Sheng, "Interest-based personalized search," ACM Transactions on Information Systems, Volume 25 Issue 1 Article NO5, 2007
  9. Kelly D and Teevan J., "Implicit feedback for inferring user preference," SIGIR Forum, 37(2), pp.18-28, 2003
  10. Feng Qiu, Junghoo Cho, "Automatic Identification of User Interest For Personalized Search," Proceedings of the 15th international conference on WWW, Session: Improved search ranking, pp.727- 736, 2006
  11. S. E. Robertson, S. Walker, "Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval," Proceedings of the 17th annual international ACM SIGIR conference, pp.232-241, 1994
  12. Karen Spärck Jones, Steve Walker, and Stephen E. Robertson. A Probabilistic Model of Information Retrieval: Development and Comparative Experiments (parts 1 and 2). Information Processing and Management, 36(6):779-840. 2000 https://doi.org/10.1016/S0306-4573(00)00015-7
  13. Stephen E. Robertson, Steve Walker, and Micheline Hancock-Beaulieu. Okapi at TREC-7. In Proceedings of the Seventh Text REtrieval Conference. Gaithersburg, USA, November 1998
  14. Jaime Teevan, Susan T. Dumais, Eric Horvitz, "Personalizing search via automated analysis of interests and activities," Proceedings of the 28th annual international ACM SIGIR conference, Session: User studies, pp.449-456, 2005
  15. Stefan Buttcher, Charles L. A. Calrke, Brad Lushman, "Term proximity scoring for ad-hoc retrieval on very large text collections," Proceedings of the 29th annual international ACM SIGIR conference, Poster Session, pp.621-622, 2006
  16. Andrea Ernst-Gerlach, Norbert Fuhr, "Retrieval in text collections with historic spelling using linguistic and spelling variants," Proceedings of the 2007 conference on Digital libraries, pp.333-341, 2007