Management Science and Financial Engineering

The Korean Operations Research and Management Science Society (한국경영과학회)
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Data Sparsity and Performance in Collaborative Filtering-based Recommendation

  • Kim Jong-Woo (School of Business, Hanyang University) ;
  • Lee Hong-Joo (Graduate School of Management, Korea Advanced Institute of Science and Technology)
  • Published : 2005.12.01
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Abstract

Collaborative filtering is one of the most common methods that e-commerce sites and Internet information services use to personalize recommendations. Collaborative filtering has the advantage of being able to use even sparse evaluation data to predict preference scores for new products. To date, however, no in-depth investigation has been conducted on how the data sparsity effect in customers' evaluation data affects collaborative filtering-based recommendation performance. In this study, we analyzed the sparsity effect and used a hybrid method based on customers' evaluations and purchases collected from an online bookstore. Results indicated that recommendation performance decreased monotonically as sparsity increased, and that performance was more sensitive to sparsity in evaluation data rather than in purchase data. Results also indicated that the hybrid use of two different types of data (customers' evaluations and purchases) helped to improve the recommendation performance when evaluation data were highly sparse.

Keywords

References

  1. Allen, C., D. Kania, and B. Yaeckel, Internet World Guide to One-to-One Web Marketing. John Wiley & Sons, Inc., New York 1998
  2. Ansari, A., S. Essegaier, and R. Kohli, 'Internet recommendation systems,' Journal of Marketing Research 37, 3 (2000), 363-375 https://doi.org/10.1509/jmkr.37.3.363.18779
  3. Autonomy. http://www.autonomy.com, 2004
  4. Balabanovic, M., and Y. Shoham, 'Content-based, collaborative recommendation', Communication of ACM 40,3 (1997), 66-72 https://doi.org/10.1145/245108.245124
  5. BeFree. http://www.befree.com, 2004
  6. Breese, J. S., D. Heckerman, and C. Kadie, 'Empirical analysis of predictive algorithms for collaborative filtering,' Technical Report, MSR- TR-98-12, Microsoft Research 1998
  7. BroadVision. http://www.broadvision.com, 2004
  8. Cho, Y. H., J. K. Kim, and S. H. Kim, 'A personalized recommender system based on web usage mining and decision tree induction,' Expert Systems with Applications 23, 3 (2002), 329-242 https://doi.org/10.1016/S0957-4174(02)00052-0
  9. Dragon, R. V., 'Recommendation systems - advice from the web,' PC Magazine (1997)
  10. Elalfi, A. E., R. Haque, M. E. Elalami, 'Extracting rules from trained neural network using GA managing E-business,' Applied Soft Computing 4, 1 (2004), 65-77 https://doi.org/10.1016/j.asoc.2003.08.004
  11. Gupta, O., M. Digiovanni, H. Norita, and K. Goldberg, 'Jester 2.0: Evaluation of a new linear time collaborative filtering algorithm,' 22nd International ACM SIGIR Conference on Research and Development in Information Retrieval, (1999), 291-292
  12. Gustos, http://www.gustos.com. 2004
  13. Kim, J. K., Y. H. Cho, W. J. Kim, J. R. Kim, and J. H. Suh, 'A personalized recommendation procedure for Internet shopping support,' Electronic Commerce Research and Applications 1, 3/4 (2001), 301-313 https://doi.org/10.1016/S1567-4223(02)00022-4
  14. Kim, J. W., B. H. Lee, M. J. Shaw, H. Chang, and M. Nelson, 'Application of decision tree induction techniques to personalized advertisements on Internet storefront,' International Journal of Electronic Commerce 5, 3 (2001), 45-62 https://doi.org/10.1080/10864415.2001.11044215
  15. Konstan, J. A., B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gordon, and J. Riedl, 'GroupLens: Applying collaborative filtering to usenet news,' Communication of the ACM 40,3 (1997), 77-87 https://doi.org/10.1145/245108.245126
  16. Lee, C. H., Y. H. Kim, and P. K. Rhee, 'Web personalization expert with combining collaborative filtering and association rule mining techniques,' Expert Systems with Applications 21,3 (2001), 131-137 https://doi.org/10.1016/S0957-4174(01)00034-3
  17. Lee, W., C. Liu, and C. Lu, 'Intelligent agent-based systems for personalized recommendation in Internet commerce,' Expert Systems with Applications 22, 4 (2002), 275-284 https://doi.org/10.1016/S0957-4174(02)00015-5
  18. Linden, G., B. Smith, and J. York, 'Amazon.com recommendations: Item-to-item collaborative filtering,' IEEE Internet Computing 7, 3 (2003), 76-80 https://doi.org/10.1109/MIC.2003.1167344
  19. Mild, A. and M. Natter, 'Collaborative filtering or regression models for Internet recommendation systems?' Journal of Targeting, Measurement and Analysis of Marketing 10, 4 (2002), 304-313 https://doi.org/10.1057/palgrave.jt.5740055
  20. Mobasher, B., R. Cooley, R., and J. Srivastava, 'Automatic personalization based on web usage mining,' Communication of the ACM 43, 8 (2000), 142-151 https://doi.org/10.1145/345124.345169
  21. Mulvenna, M. D., S. S. Anand, and A. G. Buchner, 'Personalization on the net using web mining,' Communication of the ACM 43, 8 (2000), 122-125 https://doi.org/10.1145/345124.345165
  22. Net Perception, http.//www.netperceptions.com, 2004
  23. Resnick, P., N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, 'GroupLens: An open architecture for collaborative filtering of netnews,' Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work, New York, ACM (1994),175-186
  24. Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, 'Analysis of Recommendation Algorithms for E-Commerce,' Proceedings of EC'00, Minneapolis (2000) 158-167
  25. Sarwar, B., G. Karypis, J. Konstan, and J. Riedl, 'Item-Based Collaborative Filtering Recommendation Algorithms,' Proceedings of WWW10, Hong Kong (2001), 285-295
  26. Schafer, J. B., J. Konstan, J. and J. Riedl, 'E-commerce recommendation applications,' Data Mining and Knowledge Discovery 5, 1-2 (2001), 115-153 https://doi.org/10.1023/A:1009804230409
  27. Shardanand, U. and P. Maes, 'Social information filtering: Algorithms for automating 'Word of Mouth',' Proceedings of Conference on Human Factors in Computer Systems (1995), 210-217
  28. Turban, E., D. King, J.K. Lee, and D. Viehland, Electronic Commerce 2004: A Managerial Perspective, Prentice Hall, New Jersey 2003
  29. Wei, C., R. F. Easley, and M. J. Shaw, 'Web-based Recommendation Systems for Personalized e-Commerce Shopping,' Chapter 12 in E-Business Management: Integration of Web Technologies with Business Models, Shaw, M.J. (Ed.), Kluwer Academic Publishers, Norwell, MA, (2003), 249-276