Applying Small Sample Test Statistics for Behavior-based Recommendations

  • Andreas W. Neumann
  • Andreas Geyer-Schulz
Conference paper
Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)


This contribution reports on the development of small sample test statistics for identifiying recommendations in market baskets. The main application is to lessen the cold start problem of behavior-based recommender systems by faster generating quality recommendations out of the first small samples of user behavior. The derived methods are applied in the area of library networks but are generally applicable in any consumer store setting. Analysis of market basket size at different organisational levels of German research library networks reveals that at the highest network level market basket size is considerably smaller than at the university level. The overall data volume is considerably higher. These facts motivate the development of small sample tests for the identification of non-random sample patterns. As in repeat-purchase theory the independent stochastic processes are modelled. The small sample tests are based on modelling the choice-acts of a decision maker completely without preferences by a multinomial model and combinatorial enumeration over a series of increasing event spaces. A closed form of the counting process is derived.


Recommender System Event Space Market Basket Cold Start Problem Integer Partition 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. ADOMAVICIUS, G. and TUZHILIN, A. (2005): Toward the Next Generation of Recom-mender Systems: A Survey of the State-of-the-Art and Possible Extensions. IEEE Trans-actions on Knowledge and Data Engineering, 17(6), 734-749.CrossRefGoogle Scholar
  2. ANDREWS, G.E. (1976): The Theory of Partitions. Addison-Wesley, Reading.Google Scholar
  3. ANDREWS, R.L. and MANRAI, A.K. (1998): Simulation experiments in choice simplifika-tion: The effects of task and context on forecasting performance. Journal of Marketing Research, 35(2), 198-209.CrossRefGoogle Scholar
  4. ANDREWS, R.L. and SRINIVASAN, T.C. (1995): Studying consideration effects in empirical choice models using scanner panel data. Journal of Marketing Research, 32(1), 30-41.CrossRefGoogle Scholar
  5. BECHARA, A., DAMASIO, H., TRANEL, D., and DAMASIO, A.R. (1997): Deciding Ad-vantageously Before Knowing the Advantageous Strategy. Science, 257(28), 1293-1295.CrossRefGoogle Scholar
  6. DEPPE, M., SCHWINDT, W., KUGEL, H., PLASSMANN, H., and KENNING, P. (2005): Nonlinear Response Within the Medial Prefrontal Cortex Reveal When Specific Implicit Information Influences Economic Decision Making. Journal of Neuroimaging, 15(2), 171-182.Google Scholar
  7. GEYER-SCHULZ, A. and HAHSLER, M. and NEUMANN, A. and THEDE, A. (2003a): Behavior-Based Recommender Systems as Value-Added Services for Scientific Li-braries. In: H. Bozdogan: Statistical Data Mining & Knowledge Discovery. Chapman & Hall / CRC, Boca Raton, 433-454.Google Scholar
  8. GEYER-SCHULZ, A. and NEUMANN, A. and THEDE, A. (2003b): An Architecture for Behavior-Based Library Recommender Systems. Journal of Information Technology and Libraries, 22(4).Google Scholar
  9. KOTLER, P. (1980): Marketing management: analysis, planning, and control. Prentice-Hall, Englewood Cliffs.Google Scholar
  10. MADDALA, G.S. (2001): Introduction to Econometrics. John Wiley, Chichester.Google Scholar
  11. NARAYANA, C.L. and MARKIN, R.J. (1975): Consumer Behavior and Product Performance: An Alternative Conceptualization. Journal of Marketing, 39(4), 1-6.CrossRefGoogle Scholar
  12. PRIGOGINE, I. (1962): Non-equilibrium statistical mechanics. John Wiley & Sons, New York, London.zbMATHGoogle Scholar
  13. ROTHSCHILD, M. and STIGLITZ, J. (1976): Equilibrium in Competitive Insurance Markets: An Essay on the Economics of Imperfect Information. Quarterly Journal of Economics, 90 (4),629-649.CrossRefGoogle Scholar
  14. SAMUELSON, P.A. (1938a): A Note on the Pure Theory of Consumer’s Behaviour. Econom-ica, 5(17), 61-71.Google Scholar
  15. SAMUELSON, P.A. (1938b): A Note on the Pure Theory of Consumer’s Behaviour: An Ad-dendum. Economica, 5(19), 353-354.CrossRefGoogle Scholar
  16. SAMUELSON, P.A. (1948): Consumption Theory in Terms of Revealed Preference. Econom-ica, 15(60), 243-253.Google Scholar
  17. SPENCE, M.A. (1974): Market Signaling: Information Transfer in Hiring and Related Screen-ing Processes. Harvard University Press, Cambridge, Massachusetts.Google Scholar
  18. SPIGGLE, S. and SEWALL, M.A. (1987): A Choice Sets Model of Retail Selection. Journal of Marketing, 51(2), 97-111.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Andreas W. Neumann
    • 1
  • Andreas Geyer-Schulz
    • 1
  1. 1.Institute of Information Systems and ManagementUniversität Karlsruhe (TH)KarlsruheGermany

Personalised recommendations