DOI QR코드

DOI QR Code

추천시스템을 위한 연관군집 최적화 기반 협력적 필터링 방법

An Collaborative Filtering Method based on Associative Cluster Optimization for Recommendation System

  • 이현진 (한국사이버대학교 컴퓨터정보통신학과) ;
  • 지태창 (연세대학교 컴퓨터과학과)
  • 투고 : 2010.07.23
  • 심사 : 2010.09.05
  • 발행 : 2010.09.30

초록

A marketing model is changed from a customer acquisition to customer retention and it is being moved to a way that enhances the quality of customer interaction to add value to our customers. Such personalization is emerging from this background. The Web site is accelerate the adoption of a personalization, and in contrast to the rapid growth of data, quantitative analytical experience is required. For the automated analysis of large amounts of data and the results must be passed in real time of personalization has been interested in technical problems. A recommendation algorithm is an algorithm for the implementation of personalization, which predict whether the customer preferences and purchasing using the database with new customers interested or likely to purchase. As recommended number of users increases, the algorithm increases recommendation time is the problem. In this paper, to solve this problem, a recommendation system based on clustering and dimensionality reduction is proposed. First, clusters customers with such an orientation, then shrink the dimensions of the relationship between customers to low dimensional space. Because finding neighbors for recommendations is performed at low dimensional space, the computation time is greatly reduced.

키워드

참고문헌

  1. 이재식.박석두, "장르별 협업필터링을 이용한 영화 추천 시스템의 성능 향상, " 한국지능정보시스템학회논문지, Vol. 13, No. 4, 2007, pp. 65-78.
  2. 권준희.김성림 "유비쿼터스 환경에서 상황 데이터 기반 모바일 콘텐츠 서비스를 위한 추천 기법," 디지털산업정보학회 논문지, Vol. 6, No. 2, 2010, pp. 1-9.
  3. 조동주.정경용.임기욱.이정현, "개인화 추천 시스템에서 FP-Tree를 이용한 연관 군집 방법," 한국콘텐츠학회논문지, Vol. 7, No. 10, 2007, pp. 19-26.
  4. 부종수.홍종규.박원익.김룡.김영국, "추천시스템의 성능 향상을 위한 시간스키마 적용 2단계 클러스터링 기법," 전자거래학회지, Vol. 10, No. 2, 2005, pp. 109-132.
  5. 김성림.권준희, "상황인식 정보 검색 기법을 이용한 하이브리드 협업 필터링 기법," 디지털산업정보학회 논문지, Vol. 6, No. 1, 2010, pp. 143-149.
  6. 이석준, "근접 이웃 선정 협력적 필터링 추천 시스템에서 이웃 선정 방법에 관한 연구," 한국데이터정보과학회지, Vol. 20, No, 5, 2009, pp. 809-818.
  7. 윤수진, 윤희병, "개인화 추천 시스템의 성능향상 적용 알고리즘 분석," 한국퍼지 지능시스템 학회 춘계 학술 발표 논문집, Vol. 15, No. 1, 2005, pp. 181-183.
  8. P. Resnick, N. Iacovou, M. Suchak, P. Bertorm, J. Riedl, "GroupLens: An open architecture for collaborative filtering of netnews," Proceedings of ACM Conference on Computer Supported Cooperative Work, 1994, pp. 175-186.
  9. N. Good, J. Schafer, J. Konstan, J. Borchers, B. Sarwar, J. Herlocker, J. Riedl "Combining Collaborative Filtering with Personal Agents for Better Recommendations," Conference of the American Association of Artificial Intelligence, 1999, pp. 439-446.
  10. Jonathan L. Herlocker, Joseph A. Konstan, Al Borchers, John Riedl"An algorithmic framework for performing collaborative filtering," In Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, 1999, pp. 230-237.
  11. John S. Breese, David Heckerman, Carl Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Proceedins of the 14th Conference of Uncertainty in Artificial Intelligence, 1998.
  12. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl "Application of dimensionality reduction in recommender system-a case study," ACM WebKDD Workshop, 2000.
  13. B. Sarwar, "Sparsity, Scalability, and Distribution in Recommender Systems," Ph. D. Diss., Dept. of Computer and Information Sciences, Univ. of Minesota, 2001.
  14. G. Linden, B. Smith, J. York, "Amazon. com Recommendations: Item-to-item Collaborative Filtering, " IEEE Internet Computing, Vol. 7, No. 3, 2003, pp. 76-80. https://doi.org/10.1109/MIC.2003.1167344
  15. Gui-Rong Xue, Chenxi Lin, Qiang Yang, WenSi Xi, Hua-Jun Zeng, Yong Yu, Zheng Che, "Scalable Collaborative Filtering Using Cluster-based Smoothing," Proceedings of the 2005 ACM SIGIR Conference, 2005, pp. 114-121.
  16. P. Li, S. Yamada, "A Movie Recommender System Based on Inductive Learning," IEEE Conf. on Cybernetics and Intelligent Systems, 2004, pp. 318-323.
  17. Gui-Rong Xue, Chenxi Lin, Qiang Yang, Wensi Xi, Hua-Jun Zeng, Yong Yu, and Zheng Chen August, "Scalable Collaborative Filtering using Cluster based Smoothing," Proceedings of the 2005 ACM SIGIR Conference, 2005, pp. 114-121.
  18. E. Gose, R. Johnsonbugh and S. Jost, "Pattern Recognition and Image Analysis," Prentice Hall, 1996.
  19. J. He, A. H. Tan, C. L. Tan, and S. Y. Sung, "On quantitative evaluation of clustering systems," In Weili We, Hui Xiong, and Shashi Shekhar, editors, Information Retrieval and Clustering. Kluwer Academic Publishers, 2003.
  20. C. G. Li, J. Guo, G. Chen, X. F. Nie and Z. Yang, "A Version of ISOMAP with Explicit Mapping," In Proc. of Fifth International Conference on Machine Learning and Cybernetics, 2006, pp. 3201-3206.
  21. J. B. Tenenbaum, V. de Silva and J. C. Langford, "A Global Geometric Framework for Nonlinear Dimensionality Reduction," SCIENCE, Vol. 290, 2000, pp. 2319-2323. https://doi.org/10.1126/science.290.5500.2319
  22. Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, John Riedl, "GroupLens: Applying Collaborative Filtering to Usenet News," Communications of the ACM, Vol. 40, No. 3, 1997, pp. 77-87. https://doi.org/10.1145/245108.245126
  23. http://www.grouplens.org/.

피인용 문헌

  1. 속성유사도에 따른 사회연결망 서브그룹의 군집유효성 vol.17, pp.1, 2010, https://doi.org/10.17662/ksdim.2021.17.1.075