Personalized Needs Elicitation in Web-based Configuration Systems

  • Gerold Kreutler
  • Dietmar Jannach
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 87)


The high product variety of a mass customization strategy induces a high level of complexity both from the mass-customizer’s perspective as well as from the customers’ viewpoint. In particular, a high number of different product variants and configurable features can be challenging for the end-user who is often overwhelmed during the configuration and buying process. As customers are generally not technical engineers, but rather less-experienced, they are often confused and unable to choose the product that best fits their needs. As a consequence, customers can be dissatisfied with their buying decision later on, which finally leads to frustration and a decrease of customer loyalty. Web-based product configuration systems are nowadays well-established in commercial environments and enable users to specify desired product variants typically on a technical level. Thus, they efficiently support product experts in configuring their desired product variant. However, most current systems do not take into account the fact that online configuration systems should be usable and helpful for quite heterogeneous user groups. Online customers typically have a different background in terms of experience or skills or are simply different in the way they prefer to (are able to) express their needs and requirements. Thus, we argue that the typical “one-style-fits-all” approach for needs elicitation is not adequate for customer-supplier-interaction in mass customization. As users are different, it is necessary to adapt the interaction to the customer, i.e. to take the user’s background or his capabilities into account and tailor the interaction accordingly. Within this paper, we comprehensively discuss personalization and adaptation possibilities for interactive needs elicitation in online configuration by categorizing the different levels and dimensions in a conceptual framework. Throughout, we describe adequate techniques for effectively implementing such functionality and give examples for personalization opportunities for the different levels. Finally, we discuss architectural aspects when building and maintaining such highly-adaptive web applications. Our work extends already existing work on personalization for product configuration systems. However, while most existing approaches base their adaptation features on long-term user models, we focus on (knowledge-based) techniques that allow us to personalize the interaction style also for first-time users, for which there is nearly no support in most existing systems.

Key words

Personalization Web-based Configuration Systems Needs Elicitation 


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  1. Ardissono L., Felfernig A., Friedrich G., Goy A., Jannach D., Petrone G., Schäfer R., Zanker M. 2003. A Framework for the Development of Personalized, Distributed Web-Based Configuration Systems. AI Magazine, 24(3), 93–110.Google Scholar
  2. Blecker T., Friedrich G., Kaluza B., Abdelkafi N., Kreutler G. 2005. Information and Management Systems for Product Customization. Springer, New York.Google Scholar
  3. Bridge D. 2002. Towards Conversational Recommender Systems: A Dialogue Grammar Approach. Proc. of the Workshop in Mixed-Initiative Case-Based Reasoning, Workshop Programme of the 6th European Conference in Case-Based Reasoning, 9–22.Google Scholar
  4. Carenini G., Smith J., Poole D. 2003. Towards more Conversational and Collaborative Recommender Systems. Proc. of the Intelligent User Interfaces 2003 (IUl’03), Miami, USA, 12–18.Google Scholar
  5. Felfernig A., Friedrich G., Jannach D., Zanker M. 2002. Web-based configuration of virtual private networks with multiple suppliers. Proc. of the Seventh Intl. Conference on Artificial Intelligence in Design (AID’02), Cambridge, UK, 41–62.Google Scholar
  6. Freuder E.C., Wallace R.J. 1992. Partial constraint satisfaction. Artificial Intelligence, 58, Issue 1–3, 21–70.MathSciNetCrossRefGoogle Scholar
  7. Jannach D. 2004. Advisor Suite — A knowledge-based sales advisory system. Proc. of the 16th European Conference on Artificial Intelligence (ECA12004), 720–724.Google Scholar
  8. Jannach D., Kreutler G. 2004. Building On-line Sales Assistance Systems with ADVISOR SUITE, Proc. of the Sixteenth International Conference on Software Engineering & Knowledge Engineering (SEKE 2004), Alberta Banff, Canada, June 2004, 110–117.Google Scholar
  9. Jannach D., Kreutler G. 2005. Personalized User Preference Elicitation for e-Services. Proc. of the 2005 IEEE International Conference on e-Technology, e-Commerce, and e-Service, Hong Kong, 2005.Google Scholar
  10. Kobsa A., Koenemann J., Pohl W. 2001. Personalized Hypermedia Presentation Techniques for Improving Online Customer Relationships. The Knowledge Engineering Review, 16(2), 11–155.CrossRefGoogle Scholar
  11. Krasner G.E., Pope S.T., 1998. A Description of the Model-View-Controller User Interface Paradigm in the Smalltalk-80 System. ParcPlace Systems Inc., Mountain View.Google Scholar
  12. McGinty L., Smyth B. (2002a). Deep Dialogue vs Casual Conversation in Recommender Systems. In: Ricci F., Smyth, B. (Eds). Proceedings of the Workshop on Personalization in eCommerce at the Second International Conference on Adaptive Hypermedia and Web-Based Systems (AH-02), Universidad de Malaga, Malaga, Spin, 80–89.Google Scholar
  13. McGinty L., Smyth B. (2002b). Comparison-Based Recommendation. In: Lecture Notes of Computer Science 2416, Proceedings of the 6th European Advances in Case-Based Reasoning, 575–589.Google Scholar
  14. J. Pine II J. 1993. Mass Customization: The New Frontier in Business Competition. Harvard Business School Press, Boston.Google Scholar
  15. Pu P., Faltings B., Torrens M. 2003. User-Involved Preference Elicitation. In: Eighteenth International Joint Conference on Artificial Intelligence (IJCAI’03), Workshop on Configuration, Acapulco.Google Scholar
  16. Rashid A.M., Albert I., Cosley D., Lam S.K., McLee S.M., Konstan J.A., Riedl J. 2002. Getting to Know You: Learning New User Preferences in Recommender Systems. Proc. of the Intelligent User Interfaces 2002 (IUI’02), San Francisco, USA, 127–134.Google Scholar
  17. Ricken D. 2000. Introduction: personalized views of personalization. Communications of the ACM. Special Issue on Personalization, 43(8), 26–28.Google Scholar
  18. Scheer C., Hansen T., Loos P. 2003. Erweiterung von Produktkonfiguratoren im Electronic Commerce um eine Beratungskomponente. Working Papers of the Research Group Information Systems & Management, Johannes Gutenberg-University Mainz.Google Scholar
  19. Shimazu H. (2001). ExpertClerk: Navigating Shoppers’ Buying Process with the Combination of Asking and Proposing. In: Seventeenth International Joint Conference on Artificial Intelligence (IJCAI’01), Scattle, USA, 1443–1450.Google Scholar
  20. Smyth B., Cotter P. (2002). Personalized Adaptive Navigation for Mobile Portals. In: Proceedings of the 15th European Conference on Artificial Intelligence — Prestigious Applications of Intelligent Systems (PAIS), Lyons, France.Google Scholar
  21. Thiel U., Abbate M.L., Paradiso A., Stein A., Semeraro G., Abbattista F. 2002. Intelligent Ecommerce with guiding agents based on Personalized Interaction Tools. In: Gasos J., Thoben K.-D. (Eds.). E-Business Applications, Springer, 61–76.Google Scholar
  22. Thompson C.A., Göker M.H., and Langley P. 2004. A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research, 21, 393–428.Google Scholar

Copyright information

© Springer Science+Business Media, Inc. 2006

Authors and Affiliations

  • Gerold Kreutler
    • 1
  • Dietmar Jannach
    • 1
  1. 1.Institute of Business Informatics and Application Systems, Computer Science and ManufacturingUniversity of KlagenfurtKlagenfurtAustria

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