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A Hybrid Recommendation Method based on Attributes of Items and Ratings  

Kim Byeong Man (금오공과대학교 컴퓨터공학과)
Li Qing (금오공과대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.31, no.12, 2004 , pp. 1672-1683 More about this Journal
Abstract
Recommender system is a kind of web intelligence techniques to make a daily information filtering for people. Researchers have developed collaborative recommenders (social recommenders), content-based recommenders, and some hybrid systems. In this paper, we introduce a new hybrid recommender method - ICHM where clustering techniques have been applied to the item-based collaborative filtering framework. It provides a way to integrate the content information into the collaborative filtering, which contributes to not only reducing the sparsity of data set but also solving the cold start problem. Extensive experiments have been conducted on MovieLense data to analyze the characteristics of our technique. The results show that our approach contributes to the improvement of prediction quality of the item-based collaborative filtering, especially for the cold start problem.
Keywords
Hybrid Recommender System; Item-based Collaborative Filtering; Content-based Filtering; Clustering;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Douglas B. Terry, 'A tour through tapestry,' Proc. of the ACM Conference on Organizational Computing Systems (COOCS), pp. 21-30, 1993   DOI
2 G. Salton, and C. Buckley, 'Term weighting approaches in automatic text retrieval,' Information Processing and Management, Vol.24, No.5, pp. 513-523, 1988   DOI   ScienceOn
3 S.E. Robertson and K. Sparck Jones, 'Relevance weighting of search terms,' Journal of the American Society for Information Science, Vol.27, No.3, pp.129-146, 1976   ScienceOn
4 J. Verhoeff, W. Goffman and J. Belzer, 'Inefficiency of the use of the boolean functions for information retrieval systems,' Communications of the ACM, Vol.4, pp.557-558, 1961   DOI
5 M. Kim and V.V. Raghavan, 'Adaptive concept-based retrieval using a neural network,' Proc. Of ACM-SIGIR Workshop on Mathematical/Formal Methods in IR, 2000
6 Wasfi, A. M. A., 'Collecting User Access Patterns for Building user Profiles and Collaborative Filtering,' Proc. of Int. Conf. on Intelligent User Interfaces, pp.57-64, 1999
7 J.H. Lee, M.H Kim. and Y.H. Lee, 'Ranking documents in thesaurus-based Boolean retrieval systems,' Information Processing and Management, Vol.30, No.1, pp.79-91, 1993   DOI   ScienceOn
8 P.G. Anick, J.D. Brennan, R.A. Flynn, D.R. Hanssen, B. Alvey and J.M. Robbins, 'A Direct Manipulation Interface for Boolean Information Retrieval via Natural Language Query,' Proc. of ACM-SIGIR Conf., pp.135-150, 1990   DOI
9 Y. Ogawa, T. Morita and K. Kobayashi, 'A fuzzy document retrieval system using the keyword connection matrix and a learning method,' Fuzzy sets and Systems, Vol.39, pp.163-179, 1991   DOI   ScienceOn
10 Delgado, J., Ishii, N. and Ura, T., 'Content-based Collaborative Information Filtering: Actively Learning to Classify and Recommend Documents,' Proc. of Second Int. Workshop, CIA'98, pp. 206-215, 1998
11 Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P. and Riedl, J., 'GroupLens: An open architecture for collaborative filtering of Netnews,' Proc. of ACM Conf. on Computer-Supported Cooperative Work, pp.175-186, 1994
12 D. Gupta, M. Digiovanni, H. Narita, and K. Goldberg, 'Jester 2.0: A New Linear-Time Collaborative Filtering Algorithm Applied to Jokes,' Proc. of Workshop on Recommender Systems: Algorithms and Evaluation, Aug. 1999
13 Hauver, D. B. and French,J. C, 'Flycasting: Using Collaborative Filtering to Generate a Play list for Online Radio,' Proc. of Int. Conf. on Web Delivery of Music, 2001
14 M. Claypool, A. Gokhale, T. Mirana, P. Murnikv, D. Netes and M. Sartin, 'Combing Content-Based and Collaborative Filters in an Online Newspaper,' Proc. of Workshop on Recommender Systems - Implementation and Evaluation, 1999
15 Upendra S. and Patti M,. 'Social Information Filtering: Algorithms for Automating 'Word of Mouth',' Proc. of ACM CHI'95 Conference on Human Factors in Computing Systems, pp. 210-217, 1995   DOI
16 http://www.cs.umn.edu/research/GroupLens/
17 Donna Harman., 'Overview of the third Text Retrieval Conference (TREC-3),' D. K. Harman, editor, Overview of the Third Text Retrieval Conference (TREC-3), pp. 1-19, 1994
18 J. Han and M. Kamber, Data mining: Concepts and Techniques, New York: Morgan-Kaufman, 2000
19 Sonny Han Seng Chee, Jiawei Han and Ke Wang, 'RecTree: An Efficient Collaborative Filtering Method,' Lecture Notes in Computer Science, Vol. 2114, pp.141-151, 2001
20 Q. Li and B. M. Kim, 'Clustering Approach for Hybrid Recommender System,' Proc. of the 2003 IEEE/WIC International Conference on Web Intelligence (WI 2003), pp. 33-38, 2003   DOI
21 R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classfication, Wiley-Interscience Publication, New York, pp.528-530, 2000
22 M. O'Connor and J. Herlocker, 'Clustering items for collaborative filtering,' tech. rep., University of Minnesota, department of Computer Science, Minneapolis, USA, 2000
23 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
24 Q. Li and B. M. Kim, 'An Approach for Combining Content-based and Collaborative Filters,' Proc. of the Sixth International Workshop on Information Retrieval with Asian Languages, pp. 17-24, 2003   DOI
25 M. Balabanovic and Y. Shoham, 'Fab : Content-based collaborative recommendation,' CACM, Vol.40, No.3, 1997
26 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, April 2004
27 C. Basu, H. Hirsh, and W. Cohen, 'Recommendation as Classification : Using Social and Content-Based Information in recommendation,' Proc. of AAAI, 1998
28 김병만, 이경, 김시관, 임은기, 김주연, '추천시스템을 위한 내용기반 필터링과 협력필터링의 새로운 결합 기법', 한국정보과학회논문지:소프트웨어및응용, 31권 3호, pp.332-342, 2004   과학기술학회마을
29 Popescul, A., Ungar, L. H., Pennock, D. M. and Lawrence, S., 'Probabilistic Models for Unified Collaborative and Content-Based Recommendation in Sparse-Data Environments,' Proc. of Conf. on UAI, 2001
30 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