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

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Automatic Preference Rating using User Profile in Content-based Collaborative Filtering System

내용 기반 협력적 여과 시스템에서 사용자 프로파일을 이용한 자동 선호도 평가

  • Published : 2004.08.01
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Abstract

Collaborative filtering systems based on ｛user-document｝ matrix are effective in recommending web documents to user. But they have a shortcoming of decreasing the accuracy of recommendations by the first rater problem and the sparsity. This paper proposes the automatic preference rating method that generates user profile to solve the shortcoming. The profile in this paper is content-based collaborative user profile. The content-based collaborative user profile is generated by combining a content-based user profile with a collaborative user profile by mutual information method. Collaborative user profile is based on ｛user-document｝ matrix in collaborative filtering system, thus, content-based user profile is generated by relevance feedback in content-based filtering systems. After normalizing combined content-based collaborative user profiles, it automatically rates user preference by reflecting normalized profile in ｛user-document｝matrix of collaborative filtering systems. We evaluated our method on a large database of user ratings for web document and it was certified that was more efficient than existent methods.

협력적 여과 시스템은 ｛사용자-문서｝의 행렬을 기반으로 사용자에게 웹 문서를 추천하는 데 있어서 효율적인 시스템이다. 그러나 협력적 여과 시스템은 초기 평가 문제와 희박성으로 인하여 추천의 정확도가 저하된다는 단점을 갖는다. 본 논문에서는 협력적 여과 시스템의 희박성과 초기 평가 문제를 해결하기 위하여 사용자 프로파일을 생성시킴으로써 자동으로 선호도를 평가하는 방법을 제안한다. 본 논문에서 사용하는 프로파일은 협력적 여과 시스템에서의 ｛사용자-문서｝ 행렬을 기반으로 생성된 사용자 프로파일에 내용 기반 여과 시스템에서 연관 피드백을 이용하여 생성한 사용자 프로파일을 상호정보의 방법에 의해 병합함으로써 생성한 내용 기반 협력적 사용자 프로파일이다. 생성한 내용 기반 협력적 사용자 프로파일을 정규화시키고, 정규화한 프로파일을 협력적 여과 시스템의 ｛사용자-문서｝ 행렬에 반영함으로써 자동으로 선호도를 평가한다. 제안된 방법은 사용자가 웹 문서에 대해서 선호도를 평가한 데이터베이스에서 평가되었으며, 기존의 방법보다 보다 효율적임을 증명한다.

Keywords

References

  1. B. M. Sarwar, J. A. Konstan, Al Borchers, J. Herlocker, B. Miller, J. Riedl, 'Using Filtering Agents to Improve Prediction Quality in the GroupLens Research Collaborative Filtering System,' Proceedings of the 1998 Conference on Computer Supported Cooperative Work, 1998
  2. W. S. Lee, 'Collaborative learning for recommender systems,' In Proceedings of the Conference on Machine Learning, 1997
  3. J. Delgado and N. Ishii, 'Formal Models for Learning of User Preference, a Preliminary Report', In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-99), Stockholm, Sweden, July,1999
  4. 곽미라, 조동섭, '개인화된 추천 시스템의 선호도 계산을 위한 정보 필터링', 정보과학회춘계학술발표논문집, Vol. 28, No. 1, pp. 472-474, 2001
  5. R. Raymond and J. Mooney and L. Roy, 'Content-Based Book Recommending Using Learning for Text Categorization,' Proceedings of the Fifth ACM Conference on Digital Libraries, San Antonio, TX, pp. 195-204, June, 2000
  6. M. J. Pazzani, 'A framework for collaborative, content-based and demographic filtering,' Artificial Intelligence Review, pp. 393-408, 1999 https://doi.org/10.1023/A:1006544522159
  7. M. Balabanovic and Y. Shoham, 'Fab: Content-based, collaborative recommendation,' Communication of the Association of Computing Machinery, Vol. 40, No. 3, pp. 66-72, 1997 https://doi.org/10.1145/245108.245124
  8. C. Basu and H. Hirsh and W. W. Cohen, 'Recommendation as classification:Using social and content-based information in recommendation, pp. 714-720, Madison, WI, 1998
  9. N. Good, J. B. Schafer and J. A. Konstan, A. Borchers, B. Sarwar, J. Herlocker, and J. Riedl, 'Combining collaborative filtering with personal agents for better recommendations,' In Proceedings of National Conference on Artificial Intelligence(AAAI-99), pp. 439-446, 1999
  10. I. Soboroff and C. Nicholas, 'Combining content and collaboration in text filtering,' In Proceedings of the IJCAI'99 Workshop on Machine Learning in Information filtering, pp. 86-91, 1999
  11. M. Pazzani, D. Billsus, Learning and Revising User Profiles: The Identification of Interesting Web Sites, Machine Learning, Kluwer Academic Publishers, pp. 313-331, 1997
  12. S. J. Ko and J. H. Lee, 'User Preference Mining through Collaborative Filtering and Content based Filtering in Recommender System,' Proceedings of EC_WEB2002, LNCS2455, Springer, pp. 244-253, 2002
  13. D. Billsus and M. J. Pazzani, 'Learning collaborative information filters,' In Proceedings of the International Conference on Machine Learning, 1998
  14. K. Funakoshi, T. Ohguro, 'A content-based collaborative recommender system with detailed use of evaluations,' Proceedings of the 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Vol. 1, pp. 253-256, 2000 https://doi.org/10.1109/KES.2000.885805
  15. V. Rijsbergen and C. Joost, information Retrieval, butterworths, London-second edition, 1979
  16. S. J. Ko and J. H. Lee, 'Feature Selection using Association Word Mining for Classification,' In Proceedings of the Conference on DEXA2001, LNCS2113, pp. 211-220, 2001
  17. 인하대학교, 사용자 중심의 지능형 정보 검색 시스템, 최종 연구 개발 보고서, 정보통신부, 1997
  18. R. Agrawal and R. Srikant, 'Fast Algorithms for Mining Association Rules,' Proceedings of the 20th VLDB Conference, Santiago, Chile, 1994
  19. R. Agrawal and T. Imielinski and A. Swami, 'Mining association rules between sets of items in large databases,' In Proceedings of the 1993 ACM SIGMOD Conference, Washington DC, USA, 1993 https://doi.org/10.1145/170035.170072
  20. 백준호, 최준혁, 이정현, '한국어 웹 정보검색 시스템의 정확도 향상을 위한 연관 피드백 에이전트', 한국 정보처리학회 논문지 , 제6권, 제7호, pp. 1832-1840, 1999
  21. G. Salton and M. J. McGill, Introduction to Modern Information Retrieval, McGraw-Hill, 1983
  22. John. S. Breese and C. Kadie, 'Empirical Analysis of Predictive Algorithms for Collaborative Filtering,' Proceedings of the Conference on Uncertainty in Artificial Intelligence, Madison, WI, 1998