Browse > Article

Probabilistic Reinterpretation of Collaborative Filtering Approaches Considering Cluster Information of Item Contents  

Kim, Byeong-Man (금오공과대학교 컴퓨터공학과)
Li, Qing (오공과대학교 컴퓨터공학과)
Oh, Sang-Yeop (금오공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.9, 2005 , pp. 901-911 More about this Journal
Abstract
With the development of e-commerce and the proliferation of easily accessible information, information filtering has become a popular technique to prune large information spaces so that users are directed toward those items that best meet their needs and preferences. While many collaborative filtering systems have succeeded in capturing the similarities among users or items based on ratings to provide good recommendations, there are still some challenges for them to be more efficient, especially the user bias problem, non-transitive association problem and cold start problem. Those three problems impede us to capture more accurate similarities among users or items. In this paper, we provide probabilistic model approaches for UCHM and ICHM which are suggested to solve the addressed problems in hopes of achieving better performance. In this probabilistic model, objects (users or items) are classified into groups and predictions are made for users considering the Gaussian distribution of user ratings. Experiments on a real-word data set illustrate that our proposed approach is comparable with others.
Keywords
Information Filtering; Collaborative filtering; Clustering; Probabilistic Model; Hybrid Recommender System;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Jin, R., Luo Si, Chengxiang Zhai, James P. Callan, 'Collaborative filtering with decoupled models for preferences and rating,' Proc. of the CIKM 2003, pp.309-316, 2003   DOI
2 O'Conner, M. and Herlocker, J., 'Clustering items for collaborative filtering,' Proc. of the ACM-SIGIR Workshop on Recommender Systems, 1999
3 Popescul, A., Lyle H. Ungar, David M. Pennock, and Steve Lawrence, 'Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments,' Proc. of the 17'th Conference on UAI, 2001
4 Si, Luo and Jin, Rong, 'Flexible Mixture Model for Collaborative Filtering,' Proc. of the ICML 2003, pp.704-711, 2003
5 Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P. and Riedl, J., 'GroupLens: An open architecture for collaborative filtering of Netnews,' Proc. of the ACM CSCW-94, pp. 175-186, 1994
6 Gupta, D., Digiovanni, M., H. Narita, K. Goldberg, 'Jester 2.0: Evaluation of an New Linear Time Collaborative Filtering Algorithm,' Proc. of the SIGIR-99, pp.291-292, 1999   DOI
7 Nasraoui,O. and M. Pavuluri, 'Accurate Web Recommendations Based on Profile-Specific URL-Predictor Neural Networks,' Proc. of the WWW04, 2004
8 Nasraoui,O. and M. Pavuluri, 'A Context Ultra-Sensitive Approach to High Quality Web Recommendations based on Web Usage Mining and Neural Network Committees,' Proc. of the International Web Dynamics Workshop of WWW04, 2004
9 Fisher,D., Kris Hildrum, Jason Hong, Mark Newman, Megan Thomas, Rich Vuduc, 'SWAMI: a framework for collaborative filtering algorithm development and evaluation,' Proc. of the SIGIR 2000, pp.366-368, 2000   DOI
10 Huang, Z., Hsinchun Chen and Daniel Zeng, 'Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering,' ACM TOIS, Vol. 22, No.1, 2004   DOI   ScienceOn
11 J. Herlocker, J. Konstan, A. Borchers, and J. Riedl, 'An Algorithmic Framework for Performing Collaborative Filtering,' In Proceedings of ACM SiGIR-99, 1999   DOI
12 T. M. Mitchell, Machine Learning, McGRAW-HILL, 1997
13 Han, J., and Kamber, M., Data mining: Concepts and Techniques. New York: Morgan-Kaufman, 2000
14 Bradley, P. S. and Fayyad, U.M., 'Refining Initial Points for K-Means Clustering,' Proc. of the ICML '98, pp.91-99, 1998
15 M. Balabanovic and Y. Shoham, 'Fab : Content-based collaborative recommendation,' CACM, Vol.40, No.3, 1997   DOI   ScienceOn
16 C. Basu, H. Hirsh, and W. Cohen, 'Recommendation as Classification : Using Social and Content-Based Information in recommendation,' Proc. of AAAI, 1998
17 N. Good, J.B. Schafer, J.A. Konstan, A. Borchers, B. Sarwar, J. Herlocker and J. Riedl, 'Combininig Collaborative Filtering with Personal Agents for Better Recommendations,' Proc. of the AAAI-99, 1999
18 Q. Li and B. M. Kim, 'Constructing User Profiles for Collaborative Recommender System,' Advanced Web Technologies and Applications: 6th Asia-Pacific Web Conference, J. X. Yu, X. Lin, H. Lu and Y. Zhang, eds., LNCS 3007, Springer-Verlag, pp. 100-110, 2004   DOI
19 김병만, 이경, 김시관, 임은기, 김주연 '추천시스템을 위한 내용기반 필터링과 협력필터링의 새로운 결합기법', 한국정보과학학회논문지: 소프트웨어및응용 31권 3호, pp.332-342, 2004   과학기술학회마을
20 Byeong Man Kim, Qing Li, Jong-Wan Kim and Jinsoo Kim, 'A New Collaborative Recommender System Addressing three Problems,' PRICAI 2004 : Trends in Artificial Intelligence, C. Zhang, H. W. Guesgen and W. K. Yeap, Eds., LNAI 3157, Springer-Verlag, pp.495-504, 2004   DOI
21 김병만, 이경, '항목 속성과 평가정보를 이용한 혼합 추천방법', 한국정보과학학회논문지 : 소프트웨어 및 응용 31권 12호, pp. 1672-1683, 2004
22 Breese, J. S., Heckerman, D. and Kardie, C., 'Empirical Analysis of Predictive Algorithms for Collaborative Filtering,' Proc. Of the 14th UAI, pp. 43-52, 1998
23 Upendra, S. and Patti, M., 'Social Information Filtering: Algorithms for Automating Word of Mouth,' Proc. of the ACM CHI'95 Conf. on Human Factors in Computing Systems, pp. 210-217, 1995   DOI
24 Sarwar, B. M., Karypis, G., Konstan, J. A. and Riedl, J., 'Item-based Collaborative Filtering Recommendation Algorithms,' Proc. of the Tenth Int. WWW Conf. 2001, pp. 285-295, 2001   DOI
25 Hofmann, T., 'Collaborative Filtering via Gaussian Probabilistic Latent Semantic Analysis,' Proc. of the SIGIR'03, pp. 259-266, 2003   DOI