Skip to main content
SpringerLink
Log in

Acquiring Customers' Requirementsin Electronic Commerce

  • Published:
Artificial Intelligence Review Aims and scope Submit manuscript

Abstract

A key role for Artificial Intelligencetechnology in electronic commerce is in findingproducts and services that meet a user'srequirements. This may be implemented as athree-stage process of requirementselicitation, product search, and finallyproduct presentation. Alternatively the searchof the product space may happen in tandem withthe requirements elicitation process. It isalso possible to use product presentation as amechanism to focus and give context torequirements elicitation. A variety ofdifferent approaches to this issue of matchingproducts to requirements have been explored inAI research. Thus, while the focus in thispaper is on different approaches torequirements elicitation, these related issuesof product search and product presentation arealso discussed in detail. The paper concludesby placing the other papers in this specialissue in the context of this structuredoverview.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Aamodt, A. & Plaza, E. (1994). Case-Based Reasoning: Foundational Issues, Methodological Variations, and System Approaches. Communications of the ACM (March) 7(1), 39–59.

    Google Scholar 

  • Althoff, K. D. (1992). Eine fallbasierte Lernkomponente als integrierter Bestandteil der MOLTKE Werkbank zur Diagnose technischer Systeme. DISKI 23, infix.

  • Annacker, D., Spiekermann, S. & Strobel, M. (2001). e-Privacy: Evaluating a New Search Cost in Online Environments. 14th Bled Electronic Commerce Conference.

  • Auriol, E., Manago, M., Althoff, K.-D., Wess, S. & Dittrich, S. (1994). Integrating Induction and Case-Based Reasoning: Methodological Approach and First Evaluations. In: Keane, Haton & Manago (eds.) Advances in Case-Based Reasoning, Second European Workshop (EWCBR-94), 145–156. Springer.

  • Bergmann, R. & Stahl, S. (1998). Similarity Measures for Object-Oriented Case Represen-tations. In B. Smyth & P. Cunningham (Hrsg.) Advances in Case-Based Reasoning (EWCBR'98), 25–36. Springer.

  • Bergmann, R., Breen, S., Göker, M., Manago, M. & Wess, S. (1999). Developing Indus-trial Case-Based Reasoning Applications: The INRECA Methodology. Lecture Notes in Artificial Intelligence, LNAI 1612, Springer.

  • Bergmann, R. (2002). Experience Management: Foundations, Development Methodology, and Internet-Based Applications. Springer, forthcoming.

  • Bettman, J. R. (1979). An Information Processing Theory of Customer Choice. Addison-Wesl ey.

  • Bridge, D. & Ferguson, A. (2002). An Expressive Query Language for Product Recommender Systems, this issue.

  • Burke, R., Hammond, K. & Young, B. (1996). Knowledge-Based Navigation of Complex Information Spaces. In Proceedings of the 13th National Conference on Artificial Intelli-gence AAAI9 6, 462–468. American Association for Artificial Intelligence.

  • Burke, R. (1999a). The Wasabi Personal Shopper: A Case-Based Recommender System: In Proceedings of the 11th National Conference on Innovative Applications of Artificial Intelligence, 844–849. AAAI Press.

  • Burke, R. (ed.) (2001). Case-Based Reasoning in Electronic Commerce. In Proceedings of the ICCBR'01 Workshop. http://www.iccbr.org/iccbr01/workshops.html.

  • Burke, R. (2002a). Hybrid Recommender Systems: Survey and Experiments. User Modeling and User Adapted Interaction, in press.

  • Burke, R. (2002b). Interactive Critiquing for Catalogue Navigation in E-Commerce, this issue.

  • Billsus, D. & Pazzani, M. (1998). Learning Collaborative Information Filters. Proceedings of the Fifteenth International Conference on Machine Learning, 46–54. Madison, WI: Morgan Kaufmann.

    Google Scholar 

  • Cunningham, P. & Smyth, B. (1994). A Comparison of Model-Based and Incremental Case-Based Approaches to Electronic Fault Diagnosis. In Proc. of the Case-Based Reasoning Workshop. AAAI-Press.

  • Cunningham, P., Bergmann, R., Schmitt, S., Traphoener, R., Smyth, B. & MacAnUltaigh, P. (2001). WEBSELL: Intelligent Sales Assistants for the World Wide Web. In KI – Zeitschrift für Künstliche Intelligenz,1.

  • Doyle, M. & Cunningham, P. (2000). A Dynamic Approach to Reducing Dialog in On-Line Decision Guides. In E. Blanzieri & L. Portinale (eds.) Advance in Case-Based Reasoning, Proc. of the 5th European Workshop, EWCBR-2000, 49–60. Springer LNCS, LNAI 1898.

  • Engel, J. F. & Blackwell, R. D. (1986). Consumer Behavior. CBS College Publishing: The Dryden Press.

  • Göker, M. H. & Thompson, C. A. (2000). The Adaptive Place Advisor: A Conversational Recommendation System. In Göker, M. H. (ed.) Proc. of the 8th German Workshop on Case-Based Reasoning, GWCBR-2000, DaimerChrysler Research, Ulm.

  • Hagen, P. (1999). Guided Search For eCommerce. The Forrester Report (January).

  • Howard, J. A. (1989). Buying Behavior in Marketing Strategy. Prentice Hall.

  • Hunt, E. B., Martin, J. & Stone, P. J. (1966). Experiments in Induction. Academic Press.

  • Kaufman, L. & Rousseuw, P. J. (1990). Finding Groups in Data: An Introduction to Cluster Analysis. New York: John Wiley & Sons.

    Google Scholar 

  • Kohavi, R. (1994). Bottom-Up Induction of Oblivious Readonce Decision Graphs: Strengths and Limitations. In Bergadano, F. & De. Raedt, L. (eds.) European Conference on Machine Learning. Springer.

  • Kohlmaier, A., Schmitt, S. & Bergmann R. (2001). A Similarity-Based Approach to Attribute Selection in User-Adaptive Sales Dialogs. To appear in Aha, D. W., Watson, I. & Yang, Q. (eds.) Case-Based Reasoning Research and Development. Proc. of the 4th International Conference on Case-Based Reasoning, ICCBR'01. Springer LNAI.

  • Leake, D. (1996). Case-Based Reasoning: Experiences, Lessons and Future Directions. AAAI Press.

  • McGinty, L. & Smyth, B. (2001). Collaborative Case-Based Rasoning: Applications in Personalized Route Planning. In Aha, D. & Watson, I. (eds.) Case-Based Reasoning Research and Development, Proc. of the 4th International Conference on Case-Based Reasoning, ICCBR 2001. LNAI 2080, Springer.

  • McSherry, D. (2001). Minimizing Dialog Length in Interactive Case-Based Reasoning. International Joint Conference on Artificial Intelligence, 993–998. Morgan Kaufmann.

  • McSherry, D. (2001b). Interactive Case-Based Reasoning in Sequential Diagnosis. Applied Intelligence 14, 65–76.

    Google Scholar 

  • McSherry, D. (2002). A Generalised Approach to Similarity-Based Retrieval in Recommender Systems, this issue.

  • Nicosia, F. M. (1966). Consumer Decision Processes. Engelwood, Cliffs, N.J: Prentice Hall.

    Google Scholar 

  • Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning 1, 81–106

  • Quinlan, J. R. (1993). C4.5 Programs for Machine Learning. Morgan Kaufmann.

  • Quinlan, J. R. (1996). Improved Use of Continuous Attributes in C4.5 Journal of Artificial Intelligence Research 4, 77–90.

    Google Scholar 

  • Resnick, P. & Varian, H. R. (1997). Recommender Systems. Communications of the ACM 40, 56–58.

    Google Scholar 

  • Richter, M. M. & Schmitt, S. (2001). Kundenmodellierung und Dialogführung: Eine Heraus-forderung für eCRM. In Eggert, A. & Fassott, G. (eds.) eCRM – Management der Kundenbeziehungen im Internet-Zeitalter. Schäffer-Poeschel Verlag.

  • Rosewitz, M. & Timm, U. J. (1998). Editor für elektronische Produktberatung. Wirtschaftsin-formatik 40, 21–28.

    Google Scholar 

  • Schmitt, S. & Bergmann, R. (1999). Applying Case-Based Reasoning Technology for Product Selection and Customization in Electronic Commerce Environments. 12th Bled Electronic Commerce Conference.

  • Schmitt, S. (2002). simVar: A Similarity-Influenced Question Selection Criterion for e-Sales Dialogs, this issue.

  • Schmitt, S., Jerusalem D. & Nett, T. (2000). An Acquisition Framework to Implement Ques-tioning Strategies for Query Building for CBR Systems. In Göker, M. H. (ed.) Proc. of the 8th German Workshop on Case-Based Reasoning, GWCBR-2000. DaimerChrysler Research, Ulm.

  • Shimazu, H. (2002). ExpertClerk: A Conversational Case-Based Reasoning Tool for Devel-oping Salesclerk Agents and in E-Commmerce Webshops, this issue.

  • Smyth, B. & Cotter, P. (2000). A Personalized Television Listings Service. Communications of the ACM.

  • Stahl, A., Bergmann, R. & Schmitt S. (2000). A Customization Approach for Structured Products in Electronic Shops. In Klein, S., O'Keefe, B., Gricar, J. & Podlogar, M. (eds.) Electronic Commerce – The End of the Beginning. Proc. of the 13th Bled Electronic Commerce Conference. Bled, Slovenia, June 19–21. Volume 1: Research.

  • Timm, U. J. & Rosewitz, M. (1998). Electronic Sales Assistance for Product Configuration. In Doukidis, G. J., Gricar, J. & Novak, J. (eds.) Electronic Commerce in the Informa-tion Society. Proc. of the 11th International Bled Electronic Commerce Conference.Bled, Slovenia, June 8–10. Volume 1: Research.

  • Vollrath, I., Wilke, W. & Bergmann, R. (1998). Case-Based Reasoning Support for Online Catalog Sales. IEEE Internet Computing 2, 47–54.

    Google Scholar 

  • Wilke, W. (1999). Knowledge Management for Intelligent Sales Support in Electronic Commerce. PhD Thesis, University of Kaiserslautern, DISKI 213, infix.

Download references

Authors
  1. Ralph Bergmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pádraig Cunningham
    View author publications

    You can also search for this author in PubMed Google Scholar

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bergmann, R., Cunningham, P. Acquiring Customers' Requirementsin Electronic Commerce. Artificial Intelligence Review 18, 163–193 (2002). https://doi.org/10.1023/A:1020757322687

Download citation

  • Issue Date:

  • DOI: https://doi.org/10.1023/A:1020757322687

Search

Navigation