Skip to main content
SpringerLink
Log in

Collaborative Filtering Methods for Binary Market Basket Data Analysis

  • Conference paper
  • First Online:
Active Media Technology (AMT 2001)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2252))

Included in the following conference series:

Abstract

Retail managers have been interested in learning about crosscategory purchase behavior of their customers for a fairly long time. More recently, the task of inferring cross-category relationship patterns among retail assortments is gaining attraction due to its promotional potential within recommender systems used in online environments. Collaborative Filtering algorithms are frequently used in such settings for the prediction of choices, preferences and/or ratings of online users. The fundamental assumption of such algorithms resides in the available similarity information between a specific active user and a database of all other users. We study the effects of different similarity measures, available data points per user and the number of items to be recommended on the relative predictive performance in an experiment using market basket data collected from a grocery retailer. Using various measures for evaluation of the predictive ability, we derive some clues to the proper parameterization of such systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. Proceedings of the 20th VLDB Conference, Santiago, Chile (1994)

    Google Scholar 

  2. Anderberg, M.R.: Cluster Analysis for Applications. Academic Press, New York San Francisco London (1973)

    MATH  Google Scholar 

  3. Ansari, A., Essegaier, S., Kohli, R.: Internet Recommendation Systems. Journal of Marketing Research 37 (2000) 363–375

    Article  Google Scholar 

  4. Boecker, F.: Die Bestimmung der Kaufverbundenheit von Produkten, Duncker & Humblot, Berlin (1978)

    Google Scholar 

  5. Breese, J.S., Heckerman, D., Kadie, C.: Empirical Analysis of Predictive Algorithms for Collaborative Filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence, Morgan Kaufmann Publisher, Madison, WI, (1998)

    Google Scholar 

  6. Buechter, O., Wirth, R.: Discovery of association rules over ordinal data: A new and faster algorithm and its application to basket analysis. In: Wu, X., R. Kotagiri, Korb, K.B.(eds.): Research and Development in Knowledge Discovery and Data Mining. Second Pacific-Asia Conference, PAKDD-98, Melbourne, Australia. Springer-Verlag, Berlin Heidelberg New York (1998) 36–47

    Google Scholar 

  7. Chintagunta, P.K., Haldar, S.: Investigating Purchase Timing Behavior in Two Related Product Categories. Journal of Marketing Research 35 (1998) 43–53

    Article  Google Scholar 

  8. Dickinson, R., Harris, F., Sircar, S.: Merchandise compatibility: An exploratory study of its measurement and effect on department store performance. International Review of Retail, Distribution and Consumer Research (1992) 351–379

    Google Scholar 

  9. Dubois, D., Esteva, F., Garcia, P., Godo, L., Lopez de Mantaras, R., Prade, H.: Fuzzy sets-based models in case-based reasoning. Lecture Notes in Artificial Intelligence, Vol. 1266. Springer-Verlag, Berlin Heidelberg New York (1997) 599–610

    Google Scholar 

  10. Gower, J.C.: A general coefficient of similarity and some of its properties. Biometrics 27 (1971) 857–871

    Article  Google Scholar 

  11. Hilderman, R.J., Carter, C.L., Hamilton, H.J., Cercone, N.: Mining Market Basket Data Using Share Measures and Characterized Itemsets. In: Wu, X., R. Kotagiri, Korb, K.B. (eds.): Research and Development in Knowledge Discovery and Data Mining. Second Pacific-Asia Conference, PAKDD-98, Melbourne, Australia. Springer-Verlag, Berlin Heidelberg New York (1998) 159–173

    Google Scholar 

  12. Hruschka, H., Lukanowicz, M., Buchta, Ch.: Cross-category sales promotion effects. Journal of Retailing and Consumer Services, 6 (1999) 99–105

    Article  Google Scholar 

  13. Hubalek, Z.: Coefficients of association and similarity, based on binary (presenceabsence) data: An evaluation. Biological Review 57 (1982) 669–689

    Article  Google Scholar 

  14. Julander, C.-R. Basket Analysis. A new way of analyzing scanner data. International Journal of Retail and Distribution Management 20 (1992) 10–18

    Google Scholar 

  15. Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data. An Introduction to Cluster Analysis. John Wiley & Sons, New York Chichester Brisbane Toronto Singapore (1990)

    Google Scholar 

  16. Kohonen, T.: Self-Organizing Maps. Springer-Verlag, Berlin Heidelberg New York (1997)

    MATH  Google Scholar 

  17. Manchanda, P., Ansari, A., Gupta, S.: The “shopping basket”: A model for multicategory purchase incidence decisions. Marketing Science 18 (1999) 95–114

    Article  Google Scholar 

  18. Mulhern, F.J., Leone, R.P.: Implicit price bundling of retail products: A multiproduct approach to maximizing store profitability. Journal of Marketing 55 (1991) 63–76

    Article  Google Scholar 

  19. Plaza, E., Esteva, F., Garcia, P., Godo, L., Lopez de Mantaras, R.: A Logical Approach to Case-Based Reasoning Using Similarity Relations. International Journal of Information Sciences 106 (1996) 105–122

    Article  Google Scholar 

  20. Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. Proceedings of the ACM 1994 Conference on Computer Supported Cooperative Work. Anaheim, CA (1994) 219–231

    Google Scholar 

  21. Russell, G.J., Kamakura, W.A.: Modeling multiple category brand preference with household basket data. Journal of Retailing 73 (1997) 439–461

    Article  Google Scholar 

  22. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Analysis of Recommendation Algorithms for E-commerce. Proceedings of the EC’00, Minneapolis, Minnesota (2000)

    Google Scholar 

  23. Schnedlitz, P., Reutterer, T., Joos, W.: Data-Mining und Sortimentsverbundanalyse im Einzelhandel. In: Hippner, H., Kuesters, U., Meyer, M., Wilde, K. (eds.): Handbuch Data Mining im Marketing. Knowledge Discovery in Marketing Databases. Vieweg Verlag, Wiesbaden (2001) 951–970

    Google Scholar 

  24. Walters, R.G.: Assessing the impact of retail promotions on product substitution, complementary purchase, and inter-store sales displacement. Journal of Marketing 55 (1991) 17–28

    Article  Google Scholar 

  25. Zadeh, L.A.: Similarity relations and fuzzy orderings. Information Sciences 3 (1971) 177–200

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Vienna University of Economics and B.A., Pappenheimgasse 35/5, 1200, Vienna, Austria

    Andreas Mild

  2. Simon, Kucher & Partners, Kaerntner Strasse 7, 1010, Vienna, Austria

    Thomas Reutterer

Authors
  1. Andreas Mild
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Thomas Reutterer
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science, Hong Kong Baptist University, Kowloon Tong, Hong Kong, China

    Jiming Liu , Pong C. Yuen , Chun-hung Li  & Joseph Ng , ,  & 

  2. Department of Social Informatics, Kyoto University, Sakyo-ku, 606-8501, Kyoto, Japan

    Toru Ishida

Rights and permissions

Reprints and permissions

Copyright information

© 2001 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mild, A., Reutterer, T. (2001). Collaborative Filtering Methods for Binary Market Basket Data Analysis. In: Liu, J., Yuen, P.C., Li, Ch., Ng, J., Ishida, T. (eds) Active Media Technology. AMT 2001. Lecture Notes in Computer Science, vol 2252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45336-9_35

Download citation

  • DOI: https://doi.org/10.1007/3-540-45336-9_35

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43035-3

  • Online ISBN: 978-3-540-45336-9

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation