Collaborative filtering using principal component analysis and fuzzy clustering

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 2198, Issue , 2001, Pages 394-402

  Honda, Katsuhiro ^a   Sugiura, Nobukazu ^a   Ichihashi, Hidetomo ^a   Araki, Shoichi ^b  

^a Osaka City University   (Japan)

^b PANASONIC CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

FUZZY CLUSTERING; MATRIX ALGEBRA; PRINCIPAL COMPONENT ANALYSIS;

AUTOMATED COLLABORATIVE FILTERING; INCOMPLETE DATA; INFORMATION OVERLOADS; NEW APPROACHES; NUMERICAL EXPERIMENTS; PRINCIPAL COMPONENTS; SIMULTANEOUS APPROACHES; WORD PROCESSOR;

COLLABORATIVE FILTERING;

EID: 0012017031     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45490-x_50     Document Type: Conference Paper

References (13)

1. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An Open Architecture for Collaborative Filtering of Netnews. Proc. of ACM Conference on Computer-Supported Cooperative Work (1994) 175–186
2. Konstan, J. A., Miller, B. N., Maltz, D., Herlocker, J. L., Gardon, L. R., Riedl, J.: Grouplens: Applying Collaborative Filtering to Usenet News. Communications of the ACM, Vol.40, No.3 (1997) 77–87
3. Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating “Word of Mouth”. Proc. of ACM Conference on Human Factors in Computing Systems (1995) 210–217
4. Wiberg, T.: Computation of Principal Components when Data are Missing. Proc. of 2nd Symposium on computational Statistics (1976) 229–236
5. Shibayama, T.: A PCA-Like Method for Multivariate Data with Missing Values. Japanese Journal of Educational Psychology, Vol.40 (1992) 257–265 (in Japanese)
6. Shum, H., Ikeuchi, K., Reddy, R.: Principal Component Analysis with Missing Data and its Application to Polyhedral Object Modeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 9 (1995) 854–867
7. Yamakawa, A., Honda, K., Ichihashi, H., Miyoshi, T.: Simultaneous Approach to Fuzzy Cluster, Principal Component and Multiple Regression Analysis. Proc. of International Conference on Neural Networks (1999)
8. Oh, C.-H., Komatsu, H., Honda, K., Ichihashi, H.: Fuzzy Clustering Algorithm Extracting Principal Components Independent of Subsidiary Variables. Proc. of International Conference on Neural Networks (2000)
9. Bezdek, J. C., Coray, C., Gunderson, R., Watson, J.: Detection and Characterization of Cluster Substructure 2. Fuzzy c-Varieties and Convex Combinations Thereof. SIAM J. Appl. Math., Vol.40, No.2 (1981) 358–372
10. Bezdek, J. C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York (1981)
11. Miyamoto, S., Mukaidono, M.: Fuzzy c-Means as a Regularization and Maximum Entropy Approach. Proc. of 7th International Fuzzy Systems Association World Congress, Vol.2 (1997) 86–92
12. Miyamoto, S., Takata, O., Umayahara, K.: Handling Missing Values in Fuzzy c- Means. Proc. of 3rd Asian Fuzzy Systems Symposium (1998) 139–142
13. Herlocker, J. L., Konstan, J. A., Borchers, A. Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. Proc. of Conference on Research and Development in Information Retrieval (1999)