Skip to main content

UtilSim: Iteratively Helping Users Discover Their Preferences

  • Conference paper
E-Commerce and Web Technologies (EC-Web 2013)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 152))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 72.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Pu, P., Chen, L.: User-Involved Preference Elicitation for Product Search and Recommender Systems. Ai Magazine 29, 93–103 (2008)

    Google Scholar 

  2. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender Systems Handbook, pp. 1–35 (2011)

    Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Smyth, B., Cotter, P.: A Personalized TV Listings Service for the Digital TV Age. Knowledge-Based Systems 13(2-3), 53–59 (2000)

    Article  Google Scholar 

  6. Burke, R., Hammond, K., Yound, B.: The findme approach to assisted browsing. IEEE Expert 12(4), 32–40 (1997)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

  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. Llorente, M.S., Guerrero, S.E.: Increasing retrieval quality in conversational recommenders. IEEE Trans. Knowl. Data Eng. 24(10), 1876–1888 (2012)

    Article  Google Scholar 

  11. Bridge, D., Ferguson, A.: An expressive query language for product recommender systems. Artif. Intell. Rev. 18(3-4), 269–307 (2002)

    Article  Google Scholar 

  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)

    Chapter  Google Scholar 

  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)

    Chapter  Google Scholar 

  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. Smyth, B., Mcginty, L.: The power of suggestion. In: IJCAI, pp. 127–132. Morgan Kauffman (2003)

    Google Scholar 

  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. 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. 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)

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  20. Simonson, I.: Choice Based on Reasons: The Case of Attraction and Compromise Effects. Journal of Consumer Research 16(2), 158–174 (1989)

    Article  Google Scholar 

  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. 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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gupta, S., Chakraborti, S. (2013). UtilSim: Iteratively Helping Users Discover Their Preferences. In: Huemer, C., Lops, P. (eds) E-Commerce and Web Technologies. EC-Web 2013. Lecture Notes in Business Information Processing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39878-0_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-39878-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-39877-3

  • Online ISBN: 978-3-642-39878-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics