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A Conversational Collaborative Filtering Approach to Recommendation

  • Eoin Hurrell
  • Alan F. Smeaton
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8237)

Abstract

Recent work has shown the value of treating recommendation as a conversation between user and system, which conversational recommenders have done by allowing feedback like “not as expensive as this” on recommendations. This allows a more natural alternative to content-based information access. Our research focuses on creating a viable conversational methodology for collaborative-filtering recommendation which can apply to any kind of information, especially visual. Since collaborative filtering does not have an intrinsic understanding of the items it suggests, i.e. it doesn’t understand the content, it has no obvious mechanism for conversation. Here we develop a means by which a recommender driven purely by collaborative filtering can sustain a conversation with a user and in our evaluation we show that it enables finding multimedia items that the user wants without requiring domain knowledge.

Keywords

Information Retrieval Recommender System Recommendation Algorithm Average Prediction Error Good Recommendation 
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.
    Alon, N., Awerbuch, B., Azar, Y., Patt-Shamir, B.: Tell me who I am: An interactive recommendation system. Theory of Computing Systems 45, 261–279 (2009), doi:10.1007/s00224-008-9100-7Google Scholar
  2. 2.
    Averjanova, O., Ricci, F., Nguyen, Q.N.: Map-based interaction with a conversational mobile recommender system. In: The Second International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies, UBICOMM 2008, pp. 212–218 (October 2008)Google Scholar
  3. 3.
    Bridge, D., Göker, M.H., McGinty, L., Smyth, B.: Case-based recommender systems. Knowledge Engineering Review 20(3), 315–320 (2005)Google Scholar
  4. 4.
    de Mántaras, R.L., McSherry, D., Bridge, D.G., Leake, D.B., Smyth, B., Craw, S., Faltings, B., Maher, M.L., Cox, M.T., Forbus, K.D., Keane, M.T., Aamodt, A., Watson, I.D.: Retrieval, reuse, revision and retention in case-based reasoning. Knowledge Eng. Review 20(3), 215–240 (2005)Google Scholar
  5. 5.
    Hurrell, E., Smeaton, A.F., Smyth, B.: Interactivity and multimedia in case-based recommendation. In: Proceedings of the Twenty-Fifth International Florida Artificial Intelligence Research Society (FLAIRS) Conference, pp. 347–351. AAAI Press (2012)Google Scholar
  6. 6.
    Knijnenburg, B.P., Reijmer, N.J.M., Willemsen, M.C.: Each to his own: how different users call for different interaction methods in recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, RecSys 2011, pp. 141–148. ACM, New York (2011)Google Scholar
  7. 7.
    Manning, C.D., Raghavan, P., Schtze, H.: Introduction to Information Retrieval. Cambridge University Press, New York (2008)Google Scholar
  8. 8.
    McCarthy, K., Reilly, J., McGinty, L., Smyth, B.: Thinking positively - explanatory feedback for conversational recommender systems. Technical report. In: Proceedings of the ECCBR 2004 Workshops (2004)Google Scholar
  9. 9.
    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. 1065–1065. Springer, Heidelberg (2003)Google Scholar
  10. 10.
    Ginty, L.M., Smyth, B.: Evaluating preference-based feedback in recommender systems. In: O’Neill, M., Sutcliffe, R.F.E., Ryan, C., Eaton, M., Griffith, N.J.L. (eds.) AICS 2002. LNCS (LNAI), vol. 2464, pp. 209–214. Springer, Heidelberg (2002)Google Scholar
  11. 11.
    McNee, S.M., Riedl, J., Konstan, J.A.: Making recommendations better: an analytic model for human-recommender interaction. In: CHI Extended Abstracts on Human Factors in Computing Systems, CHI EA 2006, pp. 1103–1108. ACM, New York (2006)Google Scholar
  12. 12.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)Google Scholar
  13. 13.
    Resnick, P., Varian, H.R.: Recommender systems. Communications of the ACM 40(3), 58 (1997)Google Scholar
  14. 14.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook. Springer (2011)Google Scholar
  15. 15.
    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, vol. 2, pp. 1443–1448 (2001)Google Scholar
  16. 16.
    Sinha, S., Rashmi, K.S., Sinha, R.: Beyond algorithms: An HCI perspective on recommender systems (2001)Google Scholar
  17. 17.
    Smeaton, A.F., van Rijsbergen, C.J.: The nearest neighbour problem in information retrieval: An algorithm using upperbounds. In: Proceedings of the Fourth SIGIR Conference, pp. 83–87. ACM (1981)Google Scholar
  18. 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)Google Scholar
  19. 19.
    Tunkelang, D.: Recommendations as a conversation with the user. In: ACM RecSys., pp. 11–12 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Eoin Hurrell
    • 1
  • Alan F. Smeaton
    • 1
    • 2
  1. 1.CLARITY: Centre for Sensor Web Technologies and School of ComputingDublin City UniversityDublin 9Ireland
  2. 2.INSIGHT: Big Data and Analytics Research Centre and School of ComputingDublin City UniversityDublin 9Ireland

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