Preference-Based Organization Interfaces: Aiding User Critiques in Recommender Systems

  • Li Chen
  • Pearl Pu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4511)


Users’ critiques to the current recommendation form a crucial feedback mechanism for refining their preference models and improving a system’s accuracy in recommendations that may better interest the user. In this paper, we present a novel approach to assist users in making critiques according to their stated and potentially hidden preferences. This approach is derived from our previous work on critique generation and organization techniques. Based on a collection of real user data, we conducted an experiment to compare our approach with three existing critique generation systems. Results show that our preference-based organization interface achieves the highest level of prediction accuracy in suggesting users’ intended critiques and recommendation accuracy in locating users’ target choices. In addition, it can potentially most efficiently save real users’ interaction effort in decision making.


Recommender systems user preference models critique generation organization decision support experiment 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. ACM SIGMOD, pp. 207–216 (1993)Google Scholar
  2. 2.
    Burke, R.D., Hammond, K.J., Young, B.C.: The FindMe Approach to Assisted Browsing. IEEE Expert: Intelligent Systems and Their Applications 12(4), 32–40 (1997)Google Scholar
  3. 3.
    Chen, L., Pu, P.: Evaluating Critiquing-based Recommender Agents. In: Proc. 21st AAAI, pp. 157–162 (2006)Google Scholar
  4. 4.
    Chen, L., Pu, P.: Hybrid Critiquing-based Recommender Systems. In: Proc. IUI, pp. 22–31 (2007)Google Scholar
  5. 5.
    Keeney, R., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. Cambridge University Press, Cambridge (1976)Google Scholar
  6. 6.
    McGinty, L., Smyth, B.: On the Role of Diversity in Conversational Recommender Systems. In: Proc. 5th ICCBR, pp. 276–290 (2003)Google Scholar
  7. 7.
    Payne, J.W., Bettman, J.R., Johnson, E.J.: The Adaptive Decision Maker. Cambridge University Press, Cambridge (1993)Google Scholar
  8. 8.
    Pu, P., Chen, L.: Integrating Tradeoff Support in Product Search Tools for e-commerce Sites. In: Proc. 6th ACM EC, pp. 269–278 (2005)Google Scholar
  9. 9.
    Pu, P., Chen, L.: Trust Building with Explanation Interfaces. In: Proc. IUI, pp. 93–100 (2006)Google Scholar
  10. 10.
    Pu, P., Kumar, P.: Evaluating Example-Based Search Tools. In: Proc. 5th ACM EC, pp. 208–217 (2004)Google Scholar
  11. 11.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic Critiquing. In: Proc. 7th ECCBR, pp. 763–777 (2004)Google Scholar
  12. 12.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Incremental Critiquing. In: Proc. 24th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pp. 101–114 (2004)Google Scholar
  13. 13.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Explaining Compound Critiques. Artificial Intelligence Review, vol. 24(2) (2005)Google Scholar
  14. 14.
    Thompson, C.A., Goker, M.H., Langley, P.: A Personalized System for Conversational Recommendations. Journal of Artificial Intelligence Research 21, 393–428 (2004)Google Scholar
  15. 15.
    Viappiani, P., Faltings, B., Pu, P.: Preference-based Search using Example-Critiquing with Suggestions. Journal of Artificial Intelligence Research (to appear, 2007)Google Scholar
  16. 16.
    Zhang, J., Pu, P.: A Comparative Study of Compound Critique Generation in Conversational Recommender Systems. In: Proc. AH, pp. 234–243 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Li Chen
    • 1
  • Pearl Pu
    • 1
  1. 1.Human Computer Interaction Group, School of Computer and Communication Sciences, Swiss Federal Institute of Technology in Lausanne (EPFL) 

Personalised recommendations