추천시스템을 위한 연관군집 최적화 기반 협력적 필터링 방법
An Collaborative Filtering Method based on Associative Cluster Optimization for Recommendation System
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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.
Abstract
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.
참고문헌 (23)
- 이재식, "장르별 협업필터링을 이용한 영화추천시스템의 성능 향상", 지능정보연구, 13(4), pp.65~78 (2007), 한국지능정보시스템학회
- 권준희, "유비쿼터스 환경에서 상황 데이터 기반 모바일 콘텐츠 서비스를 위한 추천 기법", (사)디지털산업정보학회 논문지, 6(2), pp.1~9 (2010), (사)디지털산업정보학회
- 조동주, "개인화 추천 시스템에서 FP-Tree를 이용한 연관 군집 방법", 한국콘텐츠학회 논문지, 7(10), pp.19~26 (2007), 한국콘텐츠학회
- 부종수, "추천시스템의 성능 향상을 위한 시간스키마 적용 2단계 클러스터링 기법", 한국전자거래학회지, 10(2), pp.109~132 (2005), 한국전자거래학회
- 김성림, "상황인식 정보 검색 기법을 이용한 하이브리드 협업 필터링 기법", (사)디지털산업정보학회 논문지, 6(1), pp.143~149 (2010), (사)디지털산업정보학회
- 이석준, "근접 이웃 선정 협력적 필터링 추천시스템에서 이웃 선정 방법에 관한 연구", 한국데이터정보과학회지, 20(5), pp.809~818 (2009), 한국데이터정보과학회
- 윤수진, "개인화 추천 시스템의 성능향상 적용 알고리즘 분석", 한국퍼지 지능시스템 학회 춘계 학술 발표 논문집, 15(1), pp.181~183 (2005)
- P. Resnick, "GroupLens: An open architecture for collaborative filtering of netnews", Proceedings of ACM Conference on Computer Supported Cooperative Work, pp.175~186 (1994)
- N. Good, "Combining Collaborative Filtering with Personal Agents for Better Recommendations", Conference of the American Association of Artificial Intelligence, pp.439~446 (1999)
- Jonathan L. Herlocker, "An algorithmic framework for performing collaborative filtering", Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp.230~237 (1999)
- John S. Breese, "Empirical analysis of predictive algorithms for collaborative filtering", Proceedins of the 14th Conference of Uncertainty in Artificial Intelligence (1998)
- B. Sarwar, "Application of dimensionality reduction in recommender system-a case study", ACM WebKDD Workshop (2000)
- B. Sarwar, "Sparsity, Scalability, and Distribution in Recommender Systems", 박사, Dept. of Computer and Information Sciences, pp.2001
- G. Linden, "Amazon. com Recommendations: Item-to-item Collaborative Filtering", IEEE Internet Computing, 7(3), pp.76~80 (2003)
- Gui-Rong Xue, "Scalable Collaborative Filtering Using Cluster-based Smoothing", Proceedings of the 2005 ACM SIGIR Conference, pp.114~121 (2005)
- P. Li, "A Movie Recommender System Based on Inductive Learning", IEEE Conf. on Cybernetics and Intelligent Systems, pp.318~323 (2004)
- Gui-Rong Xue, "Scalable Collaborative Filtering using Cluster based Smoothing", Proceedings of the 2005 ACM SIGIR Conference, pp.114~121 (2005)
- E. Gose, "Pattern Recognition and Image Analysis" (1996), Prentice Hall
- J. He, "On quantitative evaluation of clustering systems in : Information Retrieval and Clustering" (2003), Kluwer Academic Publishers
- C. G. Li, "A Version of ISOMAP with Explicit Mapping", Proc. of Fifth International Conference on Machine Learning and Cybernetics, pp.3201~3206 (2006)
- J. B. Tenenbaum, "A Global Geometric Framework for Nonlinear Dimensionality Reduction", SCIENCE, 290, pp.2319~2323 (2000)
- Joseph A. Konstan, "GroupLens: Applying Collaborative Filtering to Usenet News", Communications of the ACM, 40(3), pp.77~87 (1997)