Advertisement

A Comparative Study of Compound Critique Generation in Conversational Recommender Systems

  • Jiyong Zhang
  • Pearl Pu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4018)

Abstract

Critiquing techniques provide an easy way for users to feedback their preferences over one or several attributes of the products in a conversational recommender system. While unit critiques only allow users to critique one attribute of the products each time, a well-generated set of compound critiques enables users to input their preferences on several attributes at the same time, and can potentially shorten the interaction cycles in finding the target products. As a result, the dynamic generation of compound critiques is a critical issue for designing the critique-based conversational recommender systems. In earlier research the Apriori algorithm has been adopted to generate compound critiques from the given data set. In this paper we propose an alternative approach for generating compound critiques based on the multi-attribute utility theory (MAUT). Our approach automatically updates the weights of the product attributes as the result of the interactive critiquing process. This modification of weights is then used to determine the compound critiques according to those products with the highest utility values. Our experiments show that the compound critiques generated by this approach are more efficient in helping users find their target products than those generated by the Apriori algorithm.

Keywords

conversational recommender system critiquing compound critique multi-attribute utility theory interaction cycle 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Burke, R.D., Hammond, K.J., Young, B.C.: The FindMe approach to assisted browsing. IEEE Expert 12(4), 32–40 (1997)CrossRefGoogle Scholar
  2. 2.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Dynamic critiquing. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 763–777. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  3. 3.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Incremental critiquing. Knowledge Based Systems 18(4-5), 143–151 (2005)CrossRefGoogle Scholar
  4. 4.
    Faltings, B., Pu, P., Torrens, M., Viappiani, P.: Designing example-critiquing interaction. In: International Conference on Intelligent User Interfaces, Island of Madeira (Portugal), pp. 22–29. ACM, New York (2004)Google Scholar
  5. 5.
    McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: On the dynamic generation of compound critiques in conversational recommender systems. In: De Bra, P.M.E., Nejdl, W. (eds.) AH 2004. LNCS, vol. 3137, pp. 176–184. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th International Conference Very Large Data Bases(VLDB), pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  7. 7.
    Keeney, R.L., Raiffa, H.: Decisions with Multiple Objectives: Preferences and Value Tradeoffs. John Wiley and Sons, New York (1976)Google Scholar
  8. 8.
    Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: The automated travel assistant. In: Proceedings of the 6th International Conference on User Modeling (UM 1997) (1997)Google Scholar
  9. 9.
    Shearin, S., Lieberman, H.: Intelligent profiling by example. In: Proceedings of the Conference on Intelligent User Interfaces, pp. 145–151. ACM Press, New York (2001)CrossRefGoogle Scholar
  10. 10.
    Pu, P., Faltings, B.: Enriching buyers’ experiences: the smartclient approach. In: Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 289–296. ACM Press, New York (2000)CrossRefGoogle Scholar
  11. 11.
    Pu, P., Kumar, P.: Evaluating example-based search tools. In: Proceedings of the ACM Conference on Electronic Commerce (EC 2004), New York, USA, pp. 208–217 (2004)Google Scholar
  12. 12.
    Smyth, B., McClave, P.: Similarity vs. diversity. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080, pp. 347–361. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  13. 13.
    McGinty, L., Smyth, B.: On the role of diversity in conversational recommender systems. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 276–290. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    McCarthy, K., Reilly, J., Smyth, B., McGinty, L.: Generating diverse compound critiques. Artificial Intelligence Review 24(3-4), 339–357 (2005)CrossRefGoogle Scholar
  15. 15.
    Stolze, M.: Soft navigation in electronic product catalogs. International Journal on Digital Libraries 3(1), 60–66 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jiyong Zhang
    • 1
  • Pearl Pu
    • 1
  1. 1.Human Computer Interaction GroupEcole Polytechnique Fédérale de Lausanne (EPFL)Switzerland

Personalised recommendations