Management Science and Financial Engineering

The Korean Operations Research and Management Science Society (한국경영과학회)
권호 표지

A Model-based Collaborative Filtering Through Regularized Discriminant Analysis Using Market Basket Data

  • Lee, Jong-Seok (Department of Industrial and Manufacturing Systems Engineering Iowa State University) ;
  • Jun, Chi-Hyuck (Department of Industrial and Management Engineering, POSTECH) ;
  • Lee, Jae-Wook (Department of Industrial and Management Engineering, POSTECH) ;
  • Kim, Soo-Young (Department of Industrial and Management Engineering, POSTECH)
  • Published : 2006.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Collaborative filtering, among other recommender systems, has been known as the most successful recommendation technique. However, it requires the user-item rating data, which may not be easily available. As an alternative, some collaborative filtering algorithms have been developed recently by utilizing the market basket data in the form of the binary user-item matrix. Viewing the recommendation scheme as a two-class classification problem, we proposed a new collaborative filtering scheme using a regularized discriminant analysis applied to the binary user-item data. The proposed discriminant model was built in terms of the major principal components and was used for predicting the probability of purchasing a particular item by an active user. The proposed scheme was illustrated with two modified real data sets and its performance was compared with the existing user-based approach in terms of the recommendation precision.

Keywords

References

  1. Breese, J. S., D. Heckerman, and C. Kadie, 'Empirical analysis of predictive algorithms for collaborative filtering,' Microsoft Research Technical Report, MSR-TR-98-12, 1998
  2. Demiriz, A., 'Enhancing product recommender systems on sparse binary data,' Data Mining and Knowledge Discovery 9, 2 (2004), 147-170 https://doi.org/10.1023/B:DAMI.0000031629.31935.ac
  3. Deshpande, M. and G. Karypis, 'Item-based top-N recommendation algorithms,' ACM Transactions on Information Systems 22, 1 (2004), 143-177 https://doi.org/10.1145/963770.963776
  4. Friedman, J. H., 'Regularized discriminant analysis,' Journal of the American Statistical Association 84, 405 (1989), 165-175 https://doi.org/10.2307/2289860
  5. Goldberg, D., D. Nichols, B. Oki, and D. Terry, 'Using collaborative filtering to weave an information tapestry,' Communications of the ACM 35, 12 (1992), 61-70
  6. Goldberg, K, T. Roeder, D. Gupta, and C. Perkins, 'Eigentaste: a constant time collaborative filtering algorithm,' Information Retrieval 4, 2 (2001), 133-151 https://doi.org/10.1023/A:1011419012209
  7. Hastie, T., R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, Springer, New York, 2001
  8. Huang, Z., H. Chen, and D. Zeng, 'Applying associative retrieval techniques to alleviate the sparsity problem in collaborative filtering,' ACM Transactions on Information Systems 22, 1 (2004), 116-142 https://doi.org/10.1145/963770.963775
  9. Lee, J.-S., C.-H. Jun, J. Lee, and S. Kim, 'Classification-based collaborative filtering using market basket data,' Expert Systems with Application 29, 3 (2005), 700-704 https://doi.org/10.1016/j.eswa.2005.04.037
  10. Mild, A. and T. Reutterer, 'Collaborative filtering methods for binary market basket data analysis,' Lecture Notes in Computer Science 2252 (2001), 302-313 https://doi.org/10.1007/3-540-45336-9_35
  11. Mild, A. and T. Reutterer, 'An improved collaborative filtering approach for predicting cross-category purchase based on binary market basket data,' Journal of Retailing and Consumer Services 10, 3 (2003), 123-133 https://doi.org/10.1016/S0969-6989(03)00003-1
  12. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, 'Grou-pLens: an open architecture for collaborative filtering of netnews,' Proceedings of the ACM 1994 Conference on Computer Supported Cooperative, (1994), 175-186
  13. Shardanand, U. and P. Maes, 'Social information filtering: algorithms for automating word of mouth,' Proceedings of ACM CHI'95 Conference on Human Factors in Computing Systems, (1995), 210-217
  14. Sharma, S., Applied multivariate techniques, New York: John Wiley & Sons, (1995), 58-87
  15. Weiss, S. M. and N. Indurkhya, 'Lightweight collaborative filtering method for binary-encoded data,' Lecture Notes in Computer Science 2168 (2001), 484-491 https://doi.org/10.1007/3-540-44794-6_40