UtilSim: Iteratively Helping Users Discover Their Preferences

  • Saurabh Gupta
  • Sutanu Chakraborti
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 152)


Conversational Recommender Systems belong to a class of knowledge based systems which simulate a customer’s interaction with a shopkeeper with the help of repeated user feedback till the user settles on a product. One of the modes for getting user feedback is Preference Based Feedback, which is especially suited for novice users(having little domain knowledge), who find it easy to express preferences across products as a whole, rather than specific product features. Such kind of novice users might not be aware of the specific characteristics of the items that they may be interested in, hence, the shopkeeper/system should show them a set of products during each interaction, which can constructively stimulate their preferences, leading them to a desirable product in subsequent interactions. We propose a novel approach to conversational recommendation, UtilSim, where utilities corresponding to products get continually updated as a user iteratively interacts with the system, helping her discover her hidden preferences in the process. We show that UtilSim, which combines domain-specific “dominance” knowledge with SimRank based similarity, significantly outperforms the existing conversational approaches using Preference Based Feedback in terms of recommendation efficiency.


Knowledge based Recommendation Preference Based Feedback Utility estimation Case Based Recommendation 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pu, P., Chen, L.: User-Involved Preference Elicitation for Product Search and Recommender Systems. Ai Magazine 29, 93–103 (2008)Google Scholar
  2. 2.
    Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35 (2011)Google Scholar
  3. 3.
    Smyth, B.: Case-Based Recommendation. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 342–376. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Shimazu, H.: Expertclerk: navigating shoppers’ buying process with the combination of asking and proposing. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence, IJCAI 2001, vol. 2, pp. 1443–1448. Morgan Kaufmann Publishers Inc., San Francisco (2001)Google Scholar
  5. 5.
    Smyth, B., Cotter, P.: A Personalized TV Listings Service for the Digital TV Age. Knowledge-Based Systems 13(2-3), 53–59 (2000)CrossRefGoogle Scholar
  6. 6.
    Burke, R., Hammond, K., Yound, B.: The findme approach to assisted browsing. IEEE Expert 12(4), 32–40 (1997)CrossRefGoogle Scholar
  7. 7.
    McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Experiments in dynamic critiquing. In: Proceedings of the 10th International Conference on Intelligent User Interfaces, IUI 2005, pp. 175–182. ACM, New York (2005)Google Scholar
  8. 8.
    Reilly, J., Zhang, J., McGinty, L., Pu, P., Smyth, B.: A comparison of two compound critiquing systems. In: Proceedings of the 12th International Conference on Intelligent user Interfaces, IUI 2007, pp. 317–320. ACM, New York (2007)CrossRefGoogle Scholar
  9. 9.
    Zhang, J., Jones, N., Pu, P.: A visual interface for critiquing-based recommender systems. In: Proceedings of the 9th ACM Conference on Electronic Commerce, EC 2008, pp. 230–239. ACM, New York (2008)Google Scholar
  10. 10.
    Llorente, M.S., Guerrero, S.E.: Increasing retrieval quality in conversational recommenders. IEEE Trans. Knowl. Data Eng. 24(10), 1876–1888 (2012)CrossRefGoogle Scholar
  11. 11.
    Bridge, D., Ferguson, A.: An expressive query language for product recommender systems. Artif. Intell. Rev. 18(3-4), 269–307 (2002)CrossRefGoogle Scholar
  12. 12.
    Jeh, G., Widom, J.: Simrank: a measure of structural-context similarity. In: KDD 2002: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM Press, New York (2002)CrossRefGoogle Scholar
  13. 13.
    Mcsherry, D.: Similarity and compromise. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS (LNAI), vol. 2689, pp. 291–305. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    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)Google Scholar
  15. 15.
    Smyth, B., Mcginty, L.: The power of suggestion. In: IJCAI, pp. 127–132. Morgan Kauffman (2003)Google Scholar
  16. 16.
    Lawrence, P., Sergey, B., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University (1998)Google Scholar
  17. 17.
    Gori, M., Pucci, A.: Itemrank: a random-walk based scoring algorithm for recommender engines. In: Proceedings of the 20th International Joint Conference on Artifical Intelligence, IJCAI 2007, pp. 2766–2771. Morgan Kaufmann Publishers Inc., San Francisco (2007)Google Scholar
  18. 18.
    Teppan, E.C., Felfernig, A.: Calculating decoy items in utility-based recommendation. In: Chien, B.-C., Hong, T.-P., Chen, S.-M., Ali, M. (eds.) IEA/AIE 2009. LNCS (LNAI), vol. 5579, pp. 183–192. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Dan, A., Thomas, W.: Seeking Subjective Dominance in Multidimensional Space: An Explanation of the Asymmetric Dominance Effect. Organizational Behavior and Human Decision Processes 63(3), 223–232 (1995)CrossRefGoogle Scholar
  20. 20.
    Simonson, I.: Choice Based on Reasons: The Case of Attraction and Compromise Effects. Journal of Consumer Research 16(2), 158–174 (1989)CrossRefGoogle Scholar
  21. 21.
    Knijnenburg, B.P., Willemsen, M.C.: Understanding the effect of adaptive preference elicitation methods on user satisfaction of a recommender system. In: Proceedings of the Third ACM Conference on Recommender Systems, RecSys 2009, pp. 381–384. ACM, New York (2009)Google Scholar
  22. 22.
    Salamó, M., Reilly, J., McGinty, L., Smyth, B.: Knowledge discovery from user preferences in conversational recommendation. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 228–239. Springer, Heidelberg (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Saurabh Gupta
    • 1
  • Sutanu Chakraborti
    • 1
  1. 1.Department of Computer Science and EngineeringIndian Institute of Technology MadrasChennaiIndia

Personalised recommendations