Nikos Karacapilidis, Thomas Leckner


Adopting a mass customization strategy, enterprises often enable customers to specify their individual product wishes by using web based configurator tools. With such tools, customers can interactively and virtually create their own instance of a product. However, customers are not usually supported in a comprehensive way during the configuration process, thus facing problems such as complexity, uncertainty, and lack of knowledge. To address the above issue, this paper presents a framework that aids customers in selecting and specifying individualized products by exploiting recommendations. Having first focused on the characteristics of configurator tools and the principles of model-based configuration, we then introduce the concept of masks for product models. The main contribution of this paper is the proposal of an integrated approach for supporting model-based product configurator tools by similarity-based recommendations. Our approach in providing recommendations has been based on the widely accepted theory of Fuzzy Sets and its associated concept of similarity measures, while recommendations provided are based on the processes of stereotype definitions and dynamic customer clustering.


  1. Anderl, R. and Trippner, D. (2000). STEP - STandard for the Exchange of Product model data, Teubner, Stuttgart, Germany.
  2. Ardissono L., Felfernig A., Friedrich G., Jannach D., Zanker M. and Schäfer R. (2001). Intelligent interfaces for distributed web-based product and service configuration, In: Proc. of the 1st Asia-Pacific Conference on Web Intelligence (WI-2001), Japan 2001.
  3. Cho, Y., Kim J. and Kim, S. (2002). A personalized recommender system based on web usage mining and decision tree induction. Expert Systems with Applications, Vol. 23, pp. 329-342.
  4. Felfernig A., Friedrich G. and Jannach D. (2001). Conceptual modeling for configuration of mass customizable products, Artificial Intelligence in Engineering, Vol. 15 (2001), 2, pp. 165-176.
  5. Huffman C. and Kahn B. (1998). Variety for sale: mass customization or mass confusion. Journal of Retailing, Vol. 74, No. 4, pp. 491-513.
  6. Inakoshi H., Okamoto S., Ohta Y. and Yugami N. (2001). Effective Decision Support for Product Configuration by Using CBR, Fujitsu Labs, Japan 2001.
  7. Leckner T. (2003). Support for online configurator tools by customer communities, Proc. 2nd Intl. World Congress on Mass Customization and Personalization (MCPC'03), Munich, Germany.
  8. Leckner T. and Lacher M. (2003). Simplifying configuration through customer oriented product models, Proc. of the 14th International Conference on Engineering Design (ICED 2003), Stockholm, Sweden.
  9. Männistö T., Soininen T. and Sulonen R. (2001). Modeling Configurable Products and Software Product Families, Software Configuration Workshop (SCM-10) of ICSE01, Toronto, Canada.
  10. Nahm, U. and Mooney, R. (2002). Text Mining with Information Extraction. In Proc. of Spring Symposium on Mining Answers from Texts and Knowledge Bases, Stanford, CA, pp. 60-68.
  11. Piller F. (2001). Mass Customization. Ein wettbewerbsstrategisches Konzept im Informationszeitalter, Wiesbaden: Gabler Deutscher Universitäts-Verlag.
  12. Piller F., Koch M., Möslein K. and Schubert P. (2003). Managing High Variety: How to Overcome the Mass Confusion Phenomenon of Customer Co-Design, EURAM 2003.
  13. Pine J. (1993). Mass Customization: The New Frontier in Business Competition, Boston: Harvard Business School Press.
  14. Renneberg V., Borghoff U. (2003): Recommendations in Product Personalization: An extended Example of Pipelined Filter Combination, Proc. 2nd Intl. World Congress on Mass Customization and Personalization (MCPC'03), Munich, Germany.
  15. Rheingold H. (1998). The Virtual Community, free in the Web at: http://www.rheingold.com/vc/book/
  16. Sabin D. and Weigel R. (1998). Product Configuration Frameworks - A Survey, IEEE Intelligent Systems & their applications 13(4), pp. 42-49.
  17. Schubert P. (2000). The Participatory Electronic Product Catalog: Supporting Customer Collaboration in ECommerce Applications, In: Electronic Markets Journal, Vol.10, No.4
  18. Stegmann R., Koch M., Lacher M., Leckner T. and Renneberg V. (2003). Generating Personalized Recommendations in a Model-Based Product Configurator System, Proc. of Workshop on Configuration held in conjunction with 18th International Joint Conference on Artificial Intelligence (IJCAI-03), Acapulco, Mexico.
  19. Tiihonen J. and Soininen T. (1997). Product Configurators - Information Systems Support for Configurable Products, in: Increasing Sales Productivity through the Use of Information Technology during the Sales Visit - A Survey of the European Market, Hewson Consulting Group, 1997.
  20. Tiihonen J., Lehtonen T., Soininen T., Pulkkinen A., Sulonen R. and Ritahuhta A. (1998). Modeling configurable product families, 4th WDK Workshop on Product Structuring, Delft University of Technology, Delft, Netherlands.
  21. Wang, W.J. (1997). New similarity measures on fuzzy sets and on elements. Fuzzy Sets and Systems, Vol. 85, pp. 305-309.
  22. Wind Y., Mahajan V. and Gunther E. (2002). Convergence Marketing - Strategies for Reaching the New Hybrid Consumer, Prentice Hall, USA.

Paper Citation

in Harvard Style

Karacapilidis N. and Leckner T. (2004). A RECOMMENDATION BASED FRAMEWORK FOR ONLINE PRODUCT CONFIGURATION . In Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 4: ICEIS, ISBN 972-8865-00-7, pages 303-308. DOI: 10.5220/0002643203030308

in Bibtex Style

author={Nikos Karacapilidis and Thomas Leckner},
booktitle={Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 4: ICEIS,},

in EndNote Style

JO - Proceedings of the Sixth International Conference on Enterprise Information Systems - Volume 4: ICEIS,
SN - 972-8865-00-7
AU - Karacapilidis N.
AU - Leckner T.
PY - 2004
SP - 303
EP - 308
DO - 10.5220/0002643203030308