Why does collaborative filtering work? - Recommendation model validation and selection by analyzing bipartite random graphs

15th Workshop on Information Technology and Systems, WITS 2005

Volumn , Issue , 2005, Pages 33-38

  Huang, Zan ^a   Zeng, Daniel D ^b  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

^b UNIVERSITY OF ARIZONA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONSUMER PRODUCTS; ELECTRONIC COMMERCE; GRAPH THEORY; INPUT OUTPUT PROGRAMS; RECOMMENDER SYSTEMS;

BIPARTITE GRAPHS; COMPREHENSIVE MODEL; EXPECTED VALUES; MODEL VALIDATION; PRODUCT INTERACTION; RELATIVE PERFORMANCE; SIMULATION APPROACH; TOPOLOGICAL MODELS;

COLLABORATIVE FILTERING;

EID: 80051531169     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.2139/ssrn.894029     Document Type: Conference Paper

References (10)

1. Adomavicius, G. and Tuzhilin, A. Towards the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering, 17(6), 734-749, 2005.
2. Albert, R. and Barabási, A.-L. Statistical mechanics of complex networks. Reviews of Modern Physics, 74. 47-97, 2002.
3. Breese, J.S., Heckerman, D. and Kadie, C., Empirical analysis of predictive algorithms for collaborative filtering. in Fourteenth Conference on Uncertainty in Artificial Intelligence, (Madison, WI, 1998), Morgan Kaufmann, 43-52, 1998.
4. Deshpande, M. and Karypis, G. Item-based top-N recommendation algorithms. ACM Transactions on Information Systems, 22(1). 143-177, 2004.
5. Hofmann, T. Latent semantic models for collaborative filtering. ACM Transactions on Information Systems, 22(1). 89-115, 2004.
6. Huang, Z., Chen, H. and Zeng, D. Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering. ACM Transactions on Information Systems, 22(1). 116-142, 2004.
7. Huang, Z., Zeng, D.D. and Chen, H. Analyzing consumer-product graphs: Empirical findings and applications in recommender systems. Management Science, under review, 2005.
8. Newman, M.E.J., Strogatz, S.H. and Watts, D.J. Random graphs with arbitrary degree distributions and their applications. Physics Review, E 64. 026118, 2001.
9. Robins, G. and Alexander, M. Small worlds among interlocking directors: Network structure and distance in bipartite graphs. Computational & Mathematical Organization Theory, 10. 69-94, 2004.
10. Sarwar, B., Karypis, G., Konstan, J. and Riedl, J., Application of dimensionality reduction in recommender systems: A case study. in WebKDD Workshop at the ACM SIGKKD, 2000.