Addressing cold-start problem in recommendation systems

Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication, ICUIMC-2008

Volumn , Issue , 2008, Pages 208-211

  Lam, Xuan Nhat ^a   Vu, Thuc ^b   Le, Trong Duc ^c   Duong, Anh Duc ^a  

^a VIETNAM NATIONAL UNIVERSITY   (Viet Nam)

^b STANFORD UNIVERSITY   (United States)

^c Sungkyunkwan University   (South Korea)

Author keywords

aspect model; cold start; collaborative filtering; information filtering; three way aspect model; triadic aspect model

Indexed keywords

ASPECT MODEL; COLD START PROBLEMS; COLD-START; COLLABORATIVE FILTERING; CONTENT BASED FILTERING; CONTENT DATA; HYBRID APPROACH; HYBRID MODEL; IN-FIELD; INFORMATION FILTERING; PERSONALIZATIONS; PROBABILISTIC ASPECTS;

INFORMATION MANAGEMENT;

RECOMMENDER SYSTEMS;

EID: 70349824268     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1352793.1352837     Document Type: Conference Paper

References (20)

1. M. Benjamin, "Collaborative Filtering - A Machine Learning Perspective," University of Torronto, 2004.
2. D. Billsus and M. J. Pazzani, "Learning collaborative information filters," Proceedings of the Fifteenth International Conference on Machine Learning, pp. 46-54, 1998.
3. J. S. Breese, D. Heckerman, and C. Kadie, "Empirical analysis of predictive algorithms for collaborative filtering," Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, pp. 34-52, 1998.
4. W. W. Cohen, R. E. Schapire, and Y. Singer, "Learning to order things," Journal of Artificial Intelligence Research, 243-270, 1999.
5. GroupLens Research, www.grouplens.org
6. J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, "An algorithmic framework for performing collaborative filtering," Proceedings of the Conference on Research and Development in Information Retrieval, 1999.
7. T. Hofmann, "Probabilistic latent semantic analysis," Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence, 1999.
8. T. Hofmann and J. Puzicha, "Latent class models for collaborative filtering," Proceedings of the Sixteenth International Join Conference on Artificial Intelligence, pp. 688-693, 1999.
9. R. M. Neal and G. E. Hinton, "A new view of the EM algorithm that justifies incremental, sparse and other variants," Learning in Graphical Models, pp. 355-368, Kluwer Academic Publishers, 1998.
10. D. M. Pennock, E. Horwitz, and C. L. Giles, "Social choice theory and recommender systems: Analysis of the axiomatic foundations of collaborative filtering," Proceedings of the Seventeenth National Conference on Artificial Intelligence, pp. 729-734, 2000.
11. D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles, "Collaborative filtering by personality diagnosis: A hybrid memory - and model-based approach," Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 473-480, 2000.
12. A. Popescul, L. H. Ungar, D. M. Pennock, and S. Lawrence, "Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments," Proceedings of the Seventeenth Conference on Uncertainty in Aritificial Intelligence, 2001.
13. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedle, "Application of dimensionality reduction in recommender system - A case study," ACM WebKDD Web mining for E-Commerce Workshop, 2000.
14. B. M. Sarwar, G. Karypis, J. A. Konstan, and J. T. Riedle, "Item-based collaborative filtering recommendation algorithms," Proceedings of the Tenth International World Wide Web Conference, pp. 285-295, 2001
15. H. Thomas, "Learning What People (Don't) Want," Proceedings of the European Conference on Machine Learning (ECML), 2001.
16. L. H. Ungar and D. P. Foster, "Clustering methods for collaborative filtering,"Workshop on Recommendation Systems at the Fifteenth National Conference on Artificial Intelligence, 1998.
17. R. Jin, L. Si, and C. Zhai, "A Study of Mixture Models for Collaborative Filtering," Journal of Information Retrieval, 2006.
18. S. Park, D. Pennock, O. Madani, N. Good and D. DeCoste, "Naive filterbots for Robust Cold-start Recommendations," KDD'06, August 20-23, 2006
19. J. Kleinberg and M. Sandler, "Using Mixture Models for Collaborative Filtering," STOC'04, June 13-15, 2004.
20. E. Savia, K. Puolamaki, J. Sinkkonen, and S. Kaski, "Two-way latent grouping model for user preference prediction," Uncertainty in Artificial Intelligence 21, F. Bacchus and T. Jaakkola, Eds. Corvallis, Oregon: AUAI Press, pp. 518-525, 2005.