Supervised Learning-Based Collaborative Filtering Using Market Basket Data for the Cold-Start Problem

- Journal title : Industrial Engineering and Management Systems
- Volume 13, Issue 4, 2014, pp.421-431
- Publisher : Korean Institute of Industrial Engineers
- DOI : 10.7232/iems.2014.13.4.421

Title & Authors

Supervised Learning-Based Collaborative Filtering Using Market Basket Data for the Cold-Start Problem

Hwang, Wook-Yeon; Jun, Chi-Hyuck;

Hwang, Wook-Yeon; Jun, Chi-Hyuck;

Abstract

The market basket data in the form of a binary user-item matrix or a binary item-user matrix can be modelled as a binary classification problem. The binary logistic regression approach tackles the binary classification problem, where principal components are predictor variables. If users or items are sparse in the training data, the binary classification problem can be considered as a cold-start problem. The binary logistic regression approach may not function appropriately if the principal components are inefficient for the cold-start problem. Assuming that the market basket data can also be considered as a special regression problem whose response is either 0 or 1, we propose three supervised learning approaches: random forest regression, random forest classification, and elastic net to tackle the cold-start problem, comparing the performance in a variety of experimental settings. The experimental results show that the proposed supervised learning approaches outperform the conventional approaches.

Keywords

Market Basket Data;Cold-Start Problem;Supervised Learning-Based Collaborative Filtering;Random Forest;Elastic Net;

Language

English

References

1.

Ahn, H. J. (2008), A new similarity measure for collaborative filtering to alleviate the new user cold-starting problems, Information Sciences, 178(1), 37-51.

2.

Breese, J. S., Heckerman, D., and Kadie, C. (1998), Empirical analysis of predictive algorithms for collaborative filtering, Technical Report MSR-TR-98-12, Microsoft Research, Redmond, WA.

4.

Breiman, L., Friedman, J., Olshen, R., and Stone, C. (1999), Classification and Regression Trees, CRC Press, New York, NY.

5.

Friedman, J., Hastie, T., and Tibshirani, R. (2009), Regularization paths for generalized linear models via coordinate descent, Department of Statistics, Stanford University, Stanford, CA.

6.

Goldberg, D., Nichols, D., Oki, B., and Terry, D. (1992), Using collaborative filtering to weave an information tapestry, Communications of the ACM, 35(12), 61-70.

7.

Goldberg, K., Roeder, T., Gupta, D., and Perkins, C. (2001), Eigentaste: a constant time collaborative filtering algorithm, Information Retrieval Journal, 4(2), 133-151.

8.

Hahsler, M. (2014), recommenderlab: a framework for developing and testing recommendation algorithms, http://cran.r-project.org/web/packages/recommenderlab/vignettes/recommenderlab.pdf.

9.

Hastie, T., Tibsharani, R., and Friedman, J. (2001), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, New York, NY.

10.

Hill, W., Stead, L., Rosenstein, M., and Furnas, G. (1995), Recommending and evaluating choices in a virtual community of use, Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, Denver, CO, 194-201.

11.

Hoerl, A. E. and Kennard, R. W. (1970), Ridge regression: biased estimation for nonorthogonal problems, Technomerics, 12(1), 55-67.

12.

Hwang, W. Y. and Lee, J. S. (2013), Shifting artificial data to detect system failures, International Transactions in Operational Research, Advanced online publication, doi: 10.1111/itor.12047.

13.

Lee, C. H., Kim, Y. H., and Rhee, P. K. (2001), Web personalization expert with combining collaborative fil-tering and association rule mining technique, Expert Systems with Applications, 21(3), 131-137.

14.

Lee, J. S. and Olafsson, S. (2009), Two-way cooperative prediction for collaborative filtering recommendations, Expert Systems with Applications, 36(3), 5353-5361.

15.

Lee, J. S., Jun, C. H., Lee, J. W., and Kim, S. Y. (2005), Classification-based collaborative filtering using market basket data, Expert Systems with Applications, 29(3), 700-704.

16.

Leung, C. W., Chan, S. C., and Chung, F. (2008), An empirical study of a cross-level association rule mining approach to cold-start recommendations, Knowledge-Based Systems, 21(7), 515-529.

17.

Lika, B., Kholomvatsos, K., and Hadjiefthymiades, S. (2014), Facing the cold start problem in recommender systems, Expert Systems with Applications, 41(4), 2065-2073.

18.

Mild, A. and Reutterer, T. (2001), Collaborative filtering methods for binary market basket data analysis, Active Media Technology, Lecture Notes in Computer Science, 2252, 302-313.

19.

Mild, A. and Reutterer, T. (2003), An improved collaborative filtering approach for predicting cross-category purchase based on binary market basket data, Journal of Retailing and Consumer Services, 10(3), 123-133.

20.

Park, D. H., Kim, H. K., Choi, I. Y., and Kim, J. K. (2012), A literature review and classification of recommender systems research, Expert Systems with Applications, 39(11), 10059-10072.

21.

Park, S. T. and Chu, W. (2009), Pairwise preference regression for cold-start recommendation, Proceedings of the third ACM Conference on Recommender Systems (RecSys2009), New York, NY, 21-28.

22.

Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., and Riedl, J. (1994), GroupLens: an open architecture for collaborative filtering of netnews, Proceedings of the ACM Conference on Computer Supported Cooperative (CSCW1994), Chapel Hill, NC, 175-186.

23.

Sarwar, B., Karypis, G., Konstan, J., and Riedl, J. (2001), Item-based collaborative filtering recommendation algorithms, Proceedings of the 10th international World Wide Web Conference (WWW10), Hong Kong, 285-295.

24.

Schein, A., Popescul A., and Ungar, L. H. (2002), Methods and metrics for cold-start recommendations, Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Tampere, Finland, 253-260.

25.

Shardanand, U. and Maes, P. (1995), Social information filtering: algorithms for automating word of mouth, Proceedings of ACM Conference on Human Factors in Computing Systems (CHI1995), Vancouver, Canada, 210-217.

26.

Tibshirani, R. (1996), Regression shrinkage and selection via the lasso, Journal of Royal Statistical Society Series B: Methodological, 58(1), 267-288.