Incremental collaborative filtering for highly-scalable recommendation algorithms

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

Volumn 3488 LNAI, Issue , 2005, Pages 553-561

  Papagelis, Manos ^a,b   Rousidis, Ioannis ^b   Plexousakis, Dimitris ^a,b   Theoharopoulos, Elias ^c  

^a INSTITUTE OF COMPUTER SCIENCE   (Greece)

^b UNIVERSITY OF CRETE   (Greece)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

DATABASE SYSTEMS; INFORMATION TECHNOLOGY; ONLINE SYSTEMS; POLYNOMIALS; PROBLEM SOLVING;

COLLABORATIVE FILTERING (CF); ONLINE APPLICATION; RECOMMENDATION FORMULATION;

ALGORITHMS;

EID: 26944458097     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11425274_57     Document Type: Conference Paper

References (14)

1. Sarwar, B., Karypis, G., Konstan, J., Riedl J.: Analysis of recommendation algorithms for e-commerce. Proc. of ACM Electronic Commerce (2000)
2. Linden, G., Smith, B., York, J.: Amazon.com Recommendations: Item-to-Item Collaborative Filtering. IEEE Internet Computing, January 2003
3. Herlocker, J. L., Konstan, J. A., Riedl, J.: Explaining Collaborative Filtering Recommendations. Proc. of the ACM Conf.on CSCW (2000)
4. Breese, J. S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering. Proc. of the UAI (1998)
5. Herlocker, J. L., Konstan, J. A., Borchers, A., Riedl, J.: An Algorithmic Framework for Performing Collaborative Filtering. Proc. of ACM SIGIR (1999)
6. Sarwar, B. M., Karypis, G., Konstan, J. A., Riedl, J. T.: Application of Dimensionality Reduction in Recommender System: A Case Study. Proc. of ACM SIGKDD (2000)
7. Papagelis M, Plexousakis D.: Qualitative Analysis of User-based and Item-based Prediction Algorithms for Recommendation Agents. Proc. of CIA (2004)
8. Ungar, L., Foster, D.: Clustering Methods for Collaborative Filtering. Proc. of Workshop on Recommendation Systems, AAAI Press (1998)
9. Zeng, C., Xing, C., Zhou, L.: Similarity Measure and Instance Selection for Collaborative Filtering. Proc. of WWW (2003)
10. Sarwar, B.M., Karypis, G., Konstan, J., Riedl, J.: Incremental SVD-Based Algorithms for Highly Scaleable Recommender Systems. Proc.of ICCIT (2002).
11. Deerwester, S., Dumais, S. T., Furnas, G. W., Landauer, T. K., Harshman, R.: Indexing by Latent Semantic Analysis. JASIS 41(6) (1990)
12. Yu, K., Xu, X., Tao, J., Ester, M., Kriegel H.: Instance Selection Techniques for Memory-Based Collaborative Filtering. Proc. of SDM (2002).
13. Jung, S. Y., Kim, T.: An Incremental Similarity Computation Method in Agglomerative Hierarchical Clustering. Proc. of ISAIS (2001)
14. Pearson K.: Mathematical contribution to the theory of evolution: VII, on the correlation of characters not quantitatively measurable. Phil. Tr. R. Soc. Lond. A, 195, 1-47, 1900