Advertisement

Utility-Based Decision Tree Optimization: A Framework for Adaptive Interviewing

  • Markus Stolze
  • Michael Ströbel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2109)

Abstract

An emerging practice in e-commerce systems is to conduct interviews with buyers in order to identify their needs. The goal of such an interview is to determine sets of items that match implicit requirements. Decision trees structure the interview process by defining which question follows a given answer. One problem related to decision trees is that changes in the selling strategy or product mix require complex tree restructuring efforts. In this paper we present a framework that represents the selling strategy as a set of parameters, reflecting the preferences of sellers and buyers. This representation of the strategy can be used to generate optimized decision trees in an iterative process, which exploits information about historical buyer behavior. Furthermore, the framework also supports advanced optimization strategies such as dynamic parameter adaptation and exit risk minimization.

Keywords

Decision Tree Utility Function Recommender System User Modeling Selling Strategy 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ardissono, L., Goy, A.: Tailoring interaction with users in electronic shops. Proceedings of the 7th International Conference on User Modeling, Banff, Canada (1999) 35–44Google Scholar
  2. 2.
    Breese, J., Heckerman, D., Kadie, D.: Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (1998) 43–52Google Scholar
  3. 3.
    Breiman, L., Friedman J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth International, Belmont (1984)Google Scholar
  4. 4.
    Brijs, T., Swinnen, G., Vanhoof, K., Wets G.: A Data Mining Framework for Optimal Product Selection in Convenience Stores. Proceedings of the Eighth European Conference on Information Systems (2000), ViennaGoogle Scholar
  5. 5.
    Brusilovsky, P.: Methods and techniques for adaptive hypermedia. User Modeling and User Adapted Interaction 3 (1996): 87–129CrossRefGoogle Scholar
  6. 6.
    Burke, R.: Integrating Knowledge-based and Collaborative-filtering Recommender Systems. Proceedings AAAI-99_Workshop AI for Electronic Commerce (AIEC99), Orlando, Florida, July 18 (http://www.cs.umbc.edu/aiec/) (1999)
  7. 7.
    Dieberger, A., Höök, K.: Applying Social Navigation Principles to the Design of Shared Virtual Spaces. WebNet 1 (1999) 289–294Google Scholar
  8. 8.
    Dix, A., Patrick, A.: Query by browsing. Proceedings IDS’94: The 2nd International Workshop on User Interfaces to Databases. P. Sawyer. Lancaster, UK, Springer Verlag (1994) 236–248Google Scholar
  9. 9.
    Fischer, G., Nieper-Lemke H.: HELGON: Extending the Retrieval Reformulation Paradigm. Proceedings of ACM CHI’89_Conference on Human Factors in Computing Systems (1989) 357–362Google Scholar
  10. 10.
    Gorry, G.A., Barnett, G.O.: Experience with a model of sequential diagnosis. Computers and Biomedical Research 1 (1968) 490–507CrossRefGoogle Scholar
  11. 11.
    Heckerman, D., Breese, J.S., Rommelse, K.: Decision-theoretic troubleshooting. Communications of the ACM 38(3) (1995) 49–57CrossRefGoogle Scholar
  12. 12.
    Horvitz, E., Breese, J., Heckerman, D., Hovel, D., Rommelse, D.: The Lumiere project: Bayesian user modeling for interring the goals and needs of software users. Fourteenth Conference on Uncertainly in Artificial Intelligence, Madison, WI, July (1998)Google Scholar
  13. 13.
    Horvitz, E.: Principles of mixed-initiative user interfaces. Proceeding of the CHI 99 conference on Human factors in computing systems, May 15–20, 1999, Pittsburgh, PA USA (1999) 159–166Google Scholar
  14. 14.
    Jameson, A., Schäfer, R., Simons, J., Weis, T.: Adaptive Provision of Evaluation-Oriented Information: Tasks and Techniques. Proceedings of the 14th International Joint Conference on Artificial Intelligence (IJCAI), Montreal, August (1995) 1886–1893Google Scholar
  15. 15.
    Jun, B.H., Kim C.S., Kim, J.: A New Criterion in Selection and Discretization of Attributes for Generation of Decision Trees. IEEE Transactions on Pattern Analysis and Machine Intelligence 19(12) (1997) 1371–1375CrossRefGoogle Scholar
  16. 16.
    Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: The automated travel assistant. User modeling: Proceedings of the Sixth International Conference, UM97. A. Jameson, C. Paris and C. Tasso (eds). Vienna, New York:, Springer Wien New York (1997) 67–78Google Scholar
  17. 17.
    Nguyen, H., Haddawy, P.: The decision-theoretic video advisor. Proceedings of the AAAI Workshop on Recommender Systems, Madison, WI (1998) 77–80Google Scholar
  18. 18.
    Popp, H., Lödel, D.: Fuzzy Techniques and User Modeling in Sales Assistants. User Modeling and User-Adapted Interaction 5(3-4) (1996) 349–370CrossRefGoogle Scholar
  19. 19.
    Quinlan, J.R.: Induction of Decision Trees. Machine Learning 1(1) (1986) 81–106Google Scholar
  20. 20.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)Google Scholar
  21. 21.
    Raiffa, H., Keeney, R.: Decisions with Multiple Objectives. Wiley, New York (1976)zbMATHGoogle Scholar
  22. 22.
    Raiffa, H.: The Art and Science of Negotiation, Harvard University Press (1982)Google Scholar
  23. 23.
    Resnick, P., Varian, H.R.: Recommender Systems. Communications of the ACM 40(3) (1997) 56–58CrossRefGoogle Scholar
  24. 24.
    Rogers, S., Fiechter, C.-N., Langley, P.: An adaptive interactive agent for route advice. Proceedings of the Third International Conference on Autonomous Agents. Seattle, WA, June (1999) 198–205Google Scholar
  25. 25.
    Shardanand, U., Maes, P.: Social information filtering: automating the “word of mouth”. Proceedings of the Conference on Human Factors in Computing System (CHI’95), Denver, CO, May 7–11, (1995) 210–219.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Markus Stolze
    • 1
  • Michael Ströbel
    • 1
  1. 1.IBM Research, Zurich Research LaboratoryRüschlikonSwitzerland

Personalised recommendations