Skip to main content
SpringerLink
Log in

Enriching Case Descriptions Using Trails in Conversational Recommenders

  • Conference paper
Case-Based Reasoning Research and Development (ICCBR 2014)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8765))

Included in the following conference series:

Abstract

Case based recommenders often use similarity as a surrogate for utility. For a given user query, the most similar products are given as recommendations. Similarities are designed in such a way that they closely approximate utilities. In this paper, we propose ways of estimating robust utility estimates based on user trails. In conversational recommenders, as the users interact with the system trails are left behind. We propose ways of leveraging these trails to induce preference models of items which can be used to estimate the relative feature specific utilities of the products. We explain how case descriptions can be enriched based on these utilities. We demonstrate the effectiveness of PageRank style algorithms to induce preference models which can in turn be used in re-ranking the recommendations.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

References

  1. Bache, K., Lichman, M.: UCI machine learning repository (2013)

    Google Scholar 

  2. Bergmann, R., Richter, M.M., Schmitt, S., Stahl, A., Vollrath, I.: Utility-oriented matching: A new research direction for case-based reasoning. In: Professionelles Wissensmanagement: Erfahrungen und Visionen. Proceedings of the 1st Conference on Professional Knowledge Management. Shaker. Citeseer (2001)

    Google Scholar 

  3. Burke, R.: A case-based reasoning approach to collaborative filtering. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS (LNAI), vol. 1898, pp. 370–379. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  4. Burke, R.D.: Hybrid recommender systems: Survey and experiments. User Model. User-Adapt. Interact. 12(4), 331–370 (2002)

    Article  MATH  Google Scholar 

  5. Burke, R.D., Hammond, K.J., Young, B.C.: The findme approach to assisted browsing. IEEE Expert 12(4), 32–40 (1997)

    Article  Google Scholar 

  6. Cao, B., Shen, D., Wang, K., Yang, Q.: Clickthrough log analysis by collaborative ranking. In: AAAI (2010)

    Google Scholar 

  7. Chandar, P., Carterette, B.: Using pagerank to infer user preferences. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2012, pp. 1167–1168. ACM, New York (2012)

    Google Scholar 

  8. Chen, L., Pu, P.: Evaluating critiquing-based recommender agents. In: Proceedings of the National Conference on Artificial Intelligence, vol. 21, p. 157. AAAI Press, MIT Press, Menlo Park, Cambridge (2006)

    Google Scholar 

  9. Chen, L., Pu, P.: Critiquing-based recommenders: survey and emerging trends. User Model. User-Adapt. Interact. 22(1-2), 125–150 (2012)

    Article  Google Scholar 

  10. Dong, R., Schaal, M., O’Mahony, M.P., McCarthy, K., Smyth, B.: Opinionated product recommendation. In: Delany, S.J., Ontañón, S. (eds.) ICCBR 2013. LNCS, vol. 7969, pp. 44–58. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  11. Gupta, S., Chakraborti, S.: UtilSim: Iteratively helping users discover their preferences. In: Huemer, C., Lops, P. (eds.) EC-Web 2013. LNBIP, vol. 152, pp. 113–124. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  12. 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)

    Chapter  Google Scholar 

  13. McCarthy, K., Salem, Y., Smyth, B.: Experience-based critiquing: Reusing critiquing experiences to improve conversational recommendation. In: Bichindaritz, I., Montani, S. (eds.) ICCBR 2010. LNCS, vol. 6176, pp. 480–494. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  14. McSherry, D., Aha, D.W.: The ins and outs of critiquing. In: IJCAI, pp. 962–967 (2007)

    Google Scholar 

  15. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University (1998)

    Google Scholar 

  16. Salem, Y., Hong, J.: History-aware critiquing-based conversational recommendation. In: Proceedings of the 22nd International Conference on World Wide Web Companion, WWW 2013 Companion, Republic and Canton of Geneva, Switzerland, pp. 63–64. International World Wide Web Conferences Steering Committee (2013)

    Google Scholar 

  17. Singla, A., White, R., Huang, J.: Studying trailfinding algorithms for enhanced web search. In: Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 443–450. ACM (2010)

    Google Scholar 

  18. 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 

  19. Stahl, A.: Approximation of utility functions by learning similarity measures. In: Lenski, W. (ed.) Logic versus Approximation. LNCS, vol. 3075, pp. 150–172. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  20. Xiong, N., Funk, P.: Building similarity metrics reflecting utility in case-based reasoning. Journal of Intelligent and Fuzzy Systems 17(4), 407–416 (2006)

    MATH  Google Scholar 

  21. 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)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, IIT Madras, Chennai, 600036, India

    Skanda Raj Vasudevan & Sutanu Chakraborti

Authors
  1. Skanda Raj Vasudevan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sutanu Chakraborti
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Software Engineering, Université Laval, G1K 7P4, Québec, Canada

    Luc Lamontagne

  2. IIIA, Artificial Intelligence Research Institute CSIC, Spanish Council for Scientific Research Campus UAB, 08193, Bellaterra, Catalonia, Spain

    Enric Plaza

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Vasudevan, S.R., Chakraborti, S. (2014). Enriching Case Descriptions Using Trails in Conversational Recommenders. In: Lamontagne, L., Plaza, E. (eds) Case-Based Reasoning Research and Development. ICCBR 2014. Lecture Notes in Computer Science(), vol 8765. Springer, Cham. https://doi.org/10.1007/978-3-319-11209-1_34

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-11209-1_34

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11208-4

  • Online ISBN: 978-3-319-11209-1

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation