Browse > Article

Harmonic Mean Weight by Combining Content Based Filtering and Collaborative Filtering in a Recommender System  

정경용 (인하대학교 전자계산학과)
류중경 (대림대학 컴퓨터정보과)
강운구 (가천길대학 뉴미디어과)
이정현 (인하대학교 전자계산학과)
Publication Information
Journal of KIISE:Software and Applications / v.30, no.3_4, 2003 , pp. 239-250 More about this Journal
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.
Keywords
CRM; Collaborative Filtering; Content Based Filtering; CRM; Naive Bayes; Recommender System;
Citations & Related Records
Times Cited By KSCI : 3  (Citation Analysis)
연도 인용수 순위
1 Passani, M. and Billsus, D., 'Learning and Revising User Profiles: The Identification of Interesting Web Sites', Machine Learning, Vol.27, pp313-331, 1997   DOI
2 J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, 'An Algorithmic Framework for Performing Collaborative Filtering,' In Proceedings of ACM SiGIR-99, 1999   DOI
3 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
4 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
5 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
6 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
7 D. Billsus and M. J. Pazzani, Learning collaborative information filters, In proceedings of the International Conference on Machine Learning, 1998
8 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
9 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
10 T. Michael, Maching Learning, McGraq-Hill, pp. 154-200, 1997
11 R. Kohavi, B. Becker, and D. Sommerfield, Improving simple Bayes In Proceedings of the Europearn Conference on Machine Learning, 1997
12 M. O'Connor and J. Herlocker, Clustering Item for Collaborative Filtering, In Proceedings of the ACM SIGIR Workshop on Recommender Systems, Berkeley, CA, 1999
13 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   DOI
14 M. Balabanovic and Y. Shoham, Fab: Contentbased, collaborative recommendation, Communication of the Association of Computing Machinery, Vol. 40, No. 3, pp. 66-72, 1997   ScienceOn
15 정경용, 김진현, 이정현, 연관 사용자 군집과 베이지 안 분류를 이용한 사용자 선호도 예측 방법, 제28회 한국정보과학회 추계학술발표 논문집(II), pp. 109-111, 2001   과학기술학회마을
16 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
17 M. J. Pazzani, A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, pp, 393-408, 1999
18 R. Cooley, et al., Data Preparation for Mining World Wide Web Browsing Patterns, Knowledge and Information Systems, Vol. 1, NO. 1, 1999
19 D. Billsus and M. J. Pazzani, Learning collaborative information filters, In proceedings of the International Conference on Machine Learning, 1998
20 P. McJones, EachMovie collaborative filtering dataset, URL:http://www.research.digital.com/SRC/eachmovie, 1997
21 정영미, 정보검색론, 구미무역 출판부, 1993
22 인하대학교, 사용자 중심의 지능형 정보 검색 시스템, 최종 연구 개발 보고서, 정보통신부, 1997
23 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