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

Korean Institute of Information Scientists and Engineers (한국정보과학회)
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Harmonic Mean Weight by Combining Content Based Filtering and Collaborative Filtering in a Recommender System

내용 기반 여과와 협력적 여과의 병합을 통한 추천 시스템에서 조화 평균 가중치

  • Published : 2003.04.01
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Abstract

Recent recommender system user a method of combining collaborative filtering system and content based filtering system in order to slove the problem of the Sparsity and First-Rater in collaborative filtering system. In this paper, to make up for the prediction accuracy in hybrid Recommender system, the harmonic mean weight(CBCF_harmonic_mean) is used for calculating the user similarity weight. After setting up the threshold as 45 considering the performance of content based filtering, we apply significance weight of n/45 to user similarity weight. To estimate the performance of the proposed method, it if compared with that of combing both the existing collaborative filtering system and the content- based filtering system. As a result, it confirms that the suggested method is efficient at improving the prediction accuracy as solving problems of the exiting collaborative filtering system.

전자 상거래 분야에서 증가하고 있는 정보들 중에 사용자가 자신의 기호에 맞는 정보 만들 만을 선택하기 위해서 각 정보를 일일이 검토하기 어려운 일이다. 이를 보완하기 위해 정보 여과 기술이 사용되는데 최근 추천 시스템은 협력적 여과 시스템의 희박성과 초기 평가 문제를 해결하기 위해서 내용 기반 여과 시스템과 협력적 적과 시스템을 병합하늘 방법을 사용한다. 본 논문에서는 혼합형 추천시스템에서의 예측의 정확도를 향상시키기 위해서 조화 평균 가중치(CBCF_harmonic_mean)를 사용자 유사도 가중치를 구할 때 사용한다. 내용 기반의 성능을 고려하여 임계치 값을 45로 설정한 후, n/45의 Significance weight을 사용자 유사도 가중치에 적용한다. 제안된 방법의 성능을 평가하기 위해서 기존의 협력적 여과 시스템과 내용 기반 여과 시스템을 병합한 방법과 비교 평가하였다. 그 결과 기존의 협력적 여과 시스템의 문제점을 해결하여 예측의 정확도를 높이는데 효과적임을 확인하였다.

Keywords

References

  1. D. Billsus and M. J. Pazzani, Learning collaborative information filters, In proceedings of the International Conference on Machine Learning, 1998
  2. M. O'Connor and J. Herlocker, Clustering Item for Collaborative Filtering, In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, 1999
  3. P. Resnick, N. Iacovou, M. Sushak, P. Bergstrom, and J. Riedl. GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of the Computer Supported Collaborative Work Conference, pages 175-186, 1994 https://doi.org/10.1145/192844.192905
  4. 정경용, 김진현, 이정현, 연관 사용자 군집과 베이지 안 분류를 이용한 사용자 선호도 예측 방법, 제28회 한국정보과학회 추계학술발표 논문집(II), pp. 109-111, 2001
  5. C. Basu and H. Hirsh and W. W. Cohen, Recommendation as classification: Using social and content-based information in recommendation, In proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 714-720, Madison, WI, 1998
  6. M. J. Pazzani, A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, pp, 393-408, 1999
  7. M. Balabanovic and Y. Shoham, Fab: Contentbased, collaborative recommendation, Communication of the Association of Computing Machinery, Vol. 40, No. 3, pp. 66-72, 1997
  8. C. Basu and H. Hirsh and W. W. Cohen, Recommendation as classification: Using social and content-based information in recommendation, In proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 714-720, Madison, WI, 1998
  9. D. Billsus and M. J. Pazzani, Learning collaborative information filters, In proceedings of the International Conference on Machine Learning, 1998
  10. 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
  11. 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
  12. T. Michael, Maching Learning, McGraq-Hill, pp. 154-200, 1997
  13. R. Kohavi, B. Becker, and D. Sommerfield, Improving simple Bayes In Proceedings of the Europearn Conference on Machine Learning, 1997
  14. J. S. Breese and D. Heckerman and C. Kadie, Empirical Analysis of Predictive Algorithms for Collaborative Filtering, Proc. of the 14th Conference on Uncertainty in Artificial Intelligence, 1998
  15. J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, 'An Algorithmic Framework for Performing Collaborative Filtering,' In Proceedings of ACM SiGIR-99, 1999 https://doi.org/10.1145/312624.312682
  16. K. Y. Jung, J. K. Ryu, and J. H. Lee, A New Collaborative Filtering Method using Representative Attributes-Neighborhood and Bayesian Estimated Value, Proceedings of International Conference on Artificial Intelligence: Las Vegas, USA, June 24-27, 2002
  17. K. Y. Jung, Y. J. Park, and J. H. Lee, Integrating User Behavior Model and Collaborative Filtering Methods in Recommender Systems, International Conference on Computer and Information Science, Seoul, Korea, August 8-9, 2002
  18. K. Y. Jung, J. H. Lee, Prediction of User Preference in Recommendation System using Association User Clustering and Bayesian Estimated Value, Lecture Notes in Artificial Intelligence 2557, 15th Australian Joint Conference on Artificial Intelligence, December 2-6, 2002
  19. Passani, M. and Billsus, D., 'Learning and Revising User Profiles: The Identification of Interesting Web Sites', Machine Learning, Vol.27, pp313-331, 1997 https://doi.org/10.1023/A:1007369909943
  20. P. McJones, EachMovie collaborative filtering dataset, URL:http://www.research.digital.com/SRC/eachmovie, 1997
  21. R. Cooley, et al., Data Preparation for Mining World Wide Web Browsing Patterns, Knowledge and Information Systems, Vol. 1, NO. 1, 1999
  22. 정영미, 정보검색론, 구미무역 출판부, 1993
  23. 인하대학교, 사용자 중심의 지능형 정보 검색 시스템, 최종 연구 개발 보고서, 정보통신부, 1997