Experience-Based Critiquing: Reusing Critiquing Experiences to Improve Conversational Recommendation

  • Kevin McCarthy
  • Yasser Salem
  • Barry Smyth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6176)

Abstract

Product recommendation systems are now a key part of many e-commerce services and have proven to be a successful way to help users navigate complex product spaces. In this paper, we focus on critiquing-based recommenders, which permit users to tweak the features of recommended products in order to refine their needs and preferences. In this paper, we describe a novel approach to reusing past critiquing histories in order to improve overall recommendation efficiency.

Keywords

Recommender System Critique Pair Session Length Recommendation Process Compound Critique 
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.
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References

  1. 1.
    Bennett, J., Lanning, S.: The Netflix Prize. In: Proceedings of the KDD Cup and Workshop (2007)Google Scholar
  2. 2.
    Bridge, D.: Product Recommendation Systems: A New Direction. In: Aha, D.W., Watson, I. (eds.) ICCBR 2001. LNCS (LNAI), vol. 2080. Springer, Heidelberg (2001)Google Scholar
  3. 3.
    Burke, R., Hammond, K., Young, B.: The FindMe Approach to Assisted Browsing. Journal of IEEE Expert 12(4), 32–40 (1997)CrossRefGoogle Scholar
  4. 4.
    Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: GroupLens: Applying Collaborative Filtering to Usenet News. Communications of the ACM 40(3), 77–87 (1997)CrossRefGoogle Scholar
  5. 5.
    Linden, G., Hanks, S., Lesh, N.: Interactive assessment of user preference models: The Automated Travel Assistant. In: Jameson, A., Tasso, C.P., C. (eds.) User Modeling: Proceedings of the Sixth International Conference, pp. 67–78. Springer, Wien (1997)Google Scholar
  6. 6.
    McCarthy, K., McGinty, L., Smyth, B., Reilly, J.: On the evaluation of dynamic critiquing: A large-scale user study. In: Veloso, M., Kambhampati, S. (eds.) Proceedings of the Twentieth National Conference on Artificial Intelligence and the Seventeenth Innovative Applications of Artificial Intelligence Conference (AAAI-2005), pp. 535–540. AAAI Press / The MIT Press (2005)Google Scholar
  7. 7.
    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)Google Scholar
  8. 8.
    McGinty, L., Smyth, B.: Comparison-Based Recommendation. In: Craw, S., Preece, A.D. (eds.) ECCBR 2002. LNCS (LNAI), vol. 2416, pp. 575–589. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    McSherry, D.: Incremental Relaxation of Unsuccessful Queries. In: Funk, P., González Calero, P.A. (eds.) ECCBR 2004. LNCS (LNAI), vol. 3155, pp. 331–345. Springer, Heidelberg (2004)Google Scholar
  10. 10.
    Pu, P., Faltings, B.: Decision Tradeoff Using Example-Critiquing and Constraint Programming. Constraints 9(4), 289–310 (2004)CrossRefGoogle Scholar
  11. 11.
    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)Google Scholar
  12. 12.
    Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Incremental critiquing. Knowledge-Based Systems 18(4-5) (2005)Google Scholar
  13. 13.
    Reilly, J., Zhang, J., McGinty, L., Pu, P., Smyth, B.: A comparison of two compound critiquing systems. In: IUI 2007: Proceedings of the 12th international conference on Intelligent user interfaces, pp. 317–320. ACM Press, New York (2007)CrossRefGoogle Scholar
  14. 14.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: An open architecture for collaborative filtering of netnews. In: Proceedings of ACM Conference on Computer-Supported Cooperative Work (CSCW 1994), August 1994, pp. 175–186. ACM Press, North Carolina (1994)CrossRefGoogle Scholar
  15. 15.
    Shardanand, U., Maes, P.: Social Information Filtering: Algorithms for Automating ”word of mouth”. In: Proceedings of the SIGCHI Conference on Human factors in Computing Systems (CHI 1995), pp. 210–217. ACM Press/Addison-Wesley Publishing Co., Denver (1995)CrossRefGoogle Scholar
  16. 16.
    Shimazu, H.: ExpertClerk: Navigating Shoppers’ Buying Process with the Combination of Asking and Proposing. In: Nebel, B. (ed.) Proceedings of the Seventeenth International Joint Conference on Artificial Intelligence (IJCAI 2001), pp. 1443–1448. Morgan Kaufmann, San Francisco (2001)Google Scholar
  17. 17.
    Smyth, B., McGinty, L.: An Analysis of Feedback Strategies in Conversational Recommender Systems. In: Cunningham, P. (ed.) Proceedings of the Fourteenth National Conference on Artificial Intelligence and Cognitive Science, AICS 2003 (2003)Google Scholar
  18. 18.
    Yang, Q., Zhang, H.H., Li, T.: Mining web logs for prediction models in www caching and prefetching. In: KDD 2001: Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 473–478. ACM Press, New York (2001)CrossRefGoogle Scholar
  19. 19.
    Zhang, J., Pu, P.: A comparative study of compound critique generation in conversational recommender systems. In: Wade, V.P., Ashman, H., Smyth, B. (eds.) AH 2006. LNCS, vol. 4018, pp. 234–243. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Kevin McCarthy
    • 1
  • Yasser Salem
    • 1
  • Barry Smyth
    • 1
  1. 1.CLARITY: Centre for Sensor Web Technologies, School of Computer Science and InformaticsUniversity College DublinDublin 4Ireland

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