Mining skewed and sparse transaction data for personalized shopping recommendation

Machine Learning

Volumn 57, Issue 1-2 SPEC. ISS., 2004, Pages 35-59

  Hsu, Chun Nan ^a   Chung, Hao Hsiang ^b   Huang, Han Shen ^b  

^a INSTITUTE OF INFORMATION SCIENCE   (Taiwan)

^b NATIONAL TAIWAN UNIVERSITY   (Taiwan)

Author keywords

Collaborative filtering; Graphical models; Shopping recommendation; Transaction data; User profiles

Indexed keywords

COLLABORATIVE FILTERING; GRAPHICAL MODELS; SHOPPING RECOMMENDATION; TRANSACTION DATA; USER PROFILES;

ALGORITHMS; CUSTOMER SATISFACTION; DECISION MAKING; INTERNET; LAGRANGE MULTIPLIERS; MARKETING; MOBILE TELECOMMUNICATION SYSTEMS; SHOPPING CENTERS;

DATA MINING;

EID: 3242783300     PISSN: 08856125     EISSN: None     Source Type: Journal    
DOI: 10.1023/B:MACH.0000035471.28235.6d     Document Type: Article

References (17)

1. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499).
2. Apte, C., Liu, B., Pednault, E. P. D., & Smyth, P. (2002). Business applications of data mining. Communications of the ACM, 45:8, 49-53.
3. Billsus, D., & Pazzani, M. J. (1998). Learning collaborative information filters. In Proceedings of the Fifteenth International Conference on Machine Learning (pp. 46-54).
4. Breese, J. S., Heckerman, D., & Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. In Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (pp. 43-52).
5. Brijs, T., Goethals, B., Swinnen, G., Vanhoof, K., & Wets, G. (2000). A data mining framework for optimal product selection in retail supermarket data: The generalized PROFSET model. In Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining (pp. 300-304).
6. Cadez, I. V., Smyth, P., & Mannila, H. (2001). Probabilistic modeling of transaction data with applications to profiling, visualization, and prediction. In Proceedings of the 7th ACM International Conference on Knowledge Discovery and Data Mining (pp. 37-46).
7. Dempster, A., Laird, N., & Rubin, D. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B39, 1-37.
8. Goldberg, D., Nichols, D. Oki, B. M., & Terry, D. (1992). Using collaborative filtering to weave an information tapestry. Communications of the ACM, 35:12, 61-70.
9. Herlocker, J. L., Konstan, J. A., Borchers, A., & Riedl, J. (1999). An algorithmic framework for performing collaborative filtering. In Proceedings of the Conference on Research and Development in Information Retrieval (pp. 230-237).
10. Hofmann, T. (1999). Probabilistic latent semantic analysis. In Proceedings of the 15th Conference on Uncertainty in Artificial Intelligence (pp. 289-296).
11. Lawrence, R. D., Almasi, G. S., Kotlyar, V., Viveros, M. S., & Duri, S. (2001). Personalization of supermarket product recommendations. Data Mining and Knowledge Discovery, 5, 11-32.
12. Ling, C., & Li, C. (1998). Data mining for direct marketing: Problems and solutions. In Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (pp. 73-79).
13. Pennock, D. M., Horvitz, E., & Giles, C. L. (2000). Collaborative filtering by personality diagnosis: A Hybrid memory-and model-based approach. In Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence (pp. 473-480).
14. Popescul, A., Ungar, L., Pennock, D., & Lawrence, S. (2001). Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In Proceedings of the Seventeenth Conference on Uncertainty in Artificial Intelligence (pp. 437-444).
15. Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., & Riedl, J. (1994). GroupLens: An open architecture for collaborative filtering of netnews. In Proceedings of ACM Conference on Computer Supported Cooperative Wort (pp. 175-186).
16. Shardanand, U., & Maes, P. (1995). Social information filtering: Algorithms for automating "Word of Mouth". In Proceedings of ACM Conference on Human Factors in Computing Systems (pp. 210-217).
17. Wedel, M., & Kamakura, W. A. (1999). Market segmentation: Conceptual and methodological foundations. Kluwer Academic Publishers.