Tailoring the Interaction with Users in Electronic Shops

  • Liliana Ardissono
  • Anna Goy
Part of the CISM International Centre for Mechanical Sciences book series (CISM, volume 407)


We describe the user modeling and personalization techniques adopted in SETA, a shell supporting the construction of adaptive Web stores which customize the interactions with users, suggesting the items best fitting their needs, and adapting the description of the store catalog to their preferences and expertise. SETA uses stereotypical information to handle the user models and applies personalization rules to dynamically generate the hypertextual pages presenting products: the system adapts the graphical aspect, length and terminology used in the descriptions to the user’s receptivity, expertise and interests. Moreover, it maintains a profile associated to each person the goods are selected for, to provide multiple criteria for the selection of items, tailored to the beneficiaries’ preferences.


User Model Virtual Store Personalization Technique Electronic Shop Predictive Part 
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. Ackerman, M., Billsus, D., Gaffney, S., Hettich, S., Khoo, G., Kim, D., Klefstad, R., Lowe, C., Ludeman, A., Muramatzu, J., Omori, K., Pazzani, M., Semler, D., Starr, B., and Yap, P. (1997). Learning probabilistic user profiles. AI Magazine Summer:47–55.Google Scholar
  2. ACM (1997). Recommender systems. Communications of the ACM 40(3).Google Scholar
  3. Ardissono, L., Barbero, C., Goy, A., and Petrone, G. (1999a). An agent architecture for personalized web stores. To appear on Proceedings of the Third International Conference on Autonomous Agents.Google Scholar
  4. Ardissono, L., Goy, A., Meo, R., Petrone, G., Console, L., Lesmo, L., Simone, C., and Torasso, P. (1999b). A configurable system for the construction of adaptive virtual stores. To appear on the World Wide Web journal Google Scholar
  5. Benyon, D. (1993). Adaptive systems: a solution to usability problems. User Modeling and User-Adapted Interaction 3:65–87.CrossRefGoogle Scholar
  6. Brusilovsky, P. (1996). Methods and techniques of adaptive hypermedia. User Modeling and User-Adapted Interaction 5(2–3):87–129.CrossRefGoogle Scholar
  7. Calvi, L. (1997). Multifunctional (hyper)books: a cognitive perspective (on the user’s side). In Proceedings of the workshop “Adaptive Systems and User Modeling on the World Wide Web, 23–30.Google Scholar
  8. de Carolis, B. D. (1998). Introducing reactivity in adaptive hypertext generation. In Proceedings of the Thirteenth European Conference on Artificial Intelligence.Google Scholar
  9. Fink, J., Kobsa, A., and Nill, A. (1997). Adaptable and adaptive information access for all users, including disabled and the elderly. In Proceedings of the Sixth Conference on User Modeling, 171–173.Google Scholar
  10. Greer, J., MacKenzie, M., and Koehn, G. (1996). User models for coercion, persuasion and sales. Research Report 96–1, ARIES Laboratory, Department of Computer Science, University of Saskatchewan.Google Scholar
  11. Hirst, G., DiMarco, C., Hovy, E., and Parsons, K. (1997). Authoring and generating health-education documents that are tailored to the needs of the individual patient. In Proceedings of the Sixth Conference on User Modeling, 107–118.Google Scholar
  12. Jameson, A., Shafer, R., Simons, J., and Weis, T. (1995). Adaptive provision of evaluation-oriented information: tasks and techniques. In Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, 1886–1893.Google Scholar
  13. Joerding, T. (1998). Intelligent multimedia presentations in the web: Fun without annoyance. In Proceedings of the Seventh Worldwide Web Conference.Google Scholar
  14. Karunanithi, N., and Alspector, J. (1997). Feature-based and clique-based user models for movie selection: a comparative study. User Modeling and User-Adapted Interaction 7:279–304.CrossRefGoogle Scholar
  15. Lesmo, L., Saitta, L., and Torasso, P. (1985). Evidence combination in expert systems. International Journal of Man-Machine Studies 22:307–326.CrossRefGoogle Scholar
  16. Linden, G., Hanks, S., and Lesh, N. (1997). Interactive assessment of user preference models: The automated travel assistant). In Proceedings of the Sixth Conference on User Modeling, 67–78.Google Scholar
  17. Milosavljevic, M., and Oberlander, J. (1998). Dynamic hypertext catalogues: Helping users to help themselves. In Proceedings of the Ninth ACM Conference on Hypertext and Hypermedia.Google Scholar
  18. Milosavljevic, M., Tulloch, A., and Dale, R. (1996). Text generation in a dynamic hypertext environment. In Proceedings of the Nineteenth Australasian Computer Science Conference, 417–426.Google Scholar
  19. Popp, H., and Lödel, D. (1996). Fuzzy techniques and user modeling in sales assistants. User Modeling and User-Adapted Interaction 5:349–370.CrossRefGoogle Scholar
  20. Raskutti, B., Beitz, A., and Ward, B. (1997). A feature-based approach to recommending selections based on past preferences. User Modeling and User-Adapted Interaction 7:179–218.CrossRefGoogle Scholar
  21. Torasso, P., and Console, L. (1989). Diagnostic Problem Solving. North Oxford Academic.Google Scholar

Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Liliana Ardissono
    • 1
  • Anna Goy
    • 1
  1. 1.Dipartimento di InformaticaUniversità di TorinoTorinoItaly

Personalised recommendations