Abstract
Personalizing the product recommendation task is a major focus of research in the area of conversational recommender systems. Conversational case-based recommender systems help users to navigate through product spaces, alternatively making product suggestions and eliciting users feedback. Critiquing is a common form of feedback and incremental critiquing-based recommender system has shown its efficiency to personalize products based primarily on a quality measure. This quality measure influences the recommendation process and it is obtained by the combination of compatibility and similarity scores. In this paper, we describe new compatibility strategies whose basis is on reinforcement learning and a new feature weighting technique which is based on the user’s history of critiques. Moreover, we show that our methodology can significantly improve recommendation efficiency in comparison with the state-of-the-art approaches.
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References
Aha, D.W.: Tolerating noisy, irrelevant and novel attributes in instance-based learning algorithms. International Journal of Man-Machine Studies 36(2), 267–287 (1992)
Aha, D.W., Breslow, L.A., Muñoz-Avila, H.: Conversational Case-Based Reasoning. Applied Intelligence 14, 9–32 (2000)
Burke, R.: Interactive Critiquing for Catalog Navigation in E-Commerce. Artificial Intelligence Review 18(3-4), 245–267 (2002)
Burke, R., Hammond, K., Young, B.: Knowledge-Based Navigation of Complex Information Spaces. In: Proceedings of the 13th National Conference on Artificial Intelligence, Portland, OR, pp. 462–468. AAAI Press/MIT Press (1996)
Burke, R., Hammond, K., Young, B.C.: The FindMe Approach to Assisted Browsing. Journal of IEEE Expert 12(4), 32–40 (1997)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. Journal Machine Learning Research 7, 1–30 (2006)
Friedman, M.: The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32(200), 675–701 (1937)
Friedman, M.: A comparison of alternative tests of significance for the problem of m rankings. The Annals of Mathematical Statistics 11(1), 86–92 (1940)
Göker, M.H., Thompson, C.A.: Personalized conversational case-based recommendation. In: Blanzieri, E., Portinale, L. (eds.) EWCBR 2000. LNCS, vol. 1898, pp. 99–111. Springer, Heidelberg (2000)
Golovin, N., Rahm, E.: Reinforcement learning architecture for web recommendations. In: Proceedings of the International Conference on Information Technology: Coding and Computing, Washington, DC, USA, vol. 2, p. 398. IEEE Computer Society Press, Los Alamitos (2004)
Kaelbling, L.P., Littman, M.L., Moore, A.W.: Reinforcement Learning: A survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Kohavi, R., Langley, P., Yun, Y.: The utility of feature weighting in nearest-neighbour algorithms. In: Poster at ECML 1997 (Unpublished)
McGinty, L., Smyth, B.: Comparison-Based Recommendation. In: Craw, S. (ed.) ECCBR 2002. LNCS, vol. 2416, pp. 575–589. Springer, Heidelberg (2002)
McGinty, L., Smyth, B.: Tweaking Critiquing. In: Proceedings of the Workshop on Personalization and Web Techniques at the International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco (2003)
McSherry, D.: Similarity and Compromise. In: Ashley, K.D., Bridge, D.G. (eds.) ICCBR 2003. LNCS, vol. 2689, pp. 291–305. Springer, Heidelberg (2003)
Moon, A., Kang, T., Kim, H., Kim, H.: A service recommendation using reinforcement learning for network-based robots in ubiquitous computing environments. In: EEE International Conference on Robot & Human Interactive Communication (2007)
Reilly, J., McCarthy, K., McGinty, L., Smyth, B.: Incremental Critiquing. In: Bramer, M., Coenen, F., Allen, T. (eds.) Research and Development in Intelligent Systems XXI. Proceedings of AI 2004, Cambridge, UK, pp. 101–114. Springer, Heidelberg (2004)
Salamó, M., Reilly, J., McGinty, L., Smyth, B.: Improving incremental critiquing. In: 16th Artificial Intelligence and Cognitive Science, pp. 379–388 (2005)
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, vol. 3721, pp. 228–239. Springer, Heidelberg (2005)
Shimazu, H.: ExpertClerk: A Conversational Case-Based Reasoning Tool for Developing Salesclerk Agents in E-Commerce Webshops. Artificial Intelligence Review 18(3-4), 223–244 (2002)
Shimazu, H., Shibata, A., Nihei, K.: ExpertGuide: A Conversational Case-Based Reasoning Tool for Developing Mentors in Knowledge Spaces. Applied Intelligence 14(1), 33–48 (2002)
Smyth, B., McGinty, L.: An Analysis of Feedback Strategies in Conversational Recommender Systems. In: Cunningham, P. (ed.) Proceedings of the 14th National Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland (2003)
Smyth, B., McGinty, L.: The Power of Suggestion. In: Proceedings of the International Joint Conference on Artificial Intelligence. Morgan Kaufmann, San Francisco (2003)
Sutton, R.S., Barto, A.G.: Reinforcement Learning: An introduction. MIT Press, Cambridge (1998)
Wettschereck, D., Aha, D.W.: Weighting features. In: Aamodt, A., Veloso, M.M. (eds.) ICCBR 1995. LNCS, vol. 1010, pp. 347–358. Springer, Heidelberg (1995)
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Salamó, M., Escalera, S., Radeva, P. (2009). Quality Enhancement Based on Reinforcement Learning and Feature Weighting for a Critiquing-Based Recommender. In: McGinty, L., Wilson, D.C. (eds) Case-Based Reasoning Research and Development. ICCBR 2009. Lecture Notes in Computer Science(), vol 5650. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02998-1_22
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DOI: https://doi.org/10.1007/978-3-642-02998-1_22
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