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Issue No.10 - Oct. (2012 vol.24)
pp: 1876-1888
Maria Salamó Llorente , Universitat de Barcelona, Barcelona
Sergio Escalera Guerrero , Universitat de Barcelona, Barcelona
A major task of research in conversational recommender systems is personalization. Critiquing is a common and powerful form of feedback, where a user can express her feature preferences by applying a series of directional critiques over the recommendations instead of providing specific preference values. Incremental Critiquing (IC) is a conversational recommender system that uses critiquing as a feedback to efficiently personalize products. The expectation is that in each cycle the system retrieves the products that best satisfy the user's soft product preferences from a minimal information input. In this paper, we present a novel technique that increases retrieval quality based on a combination of compatibility and similarity scores. Under the hypothesis that a user learns during the recommendation process, we propose two novel exponential Reinforcement Learning (RL) approaches for compatibility that take into account both the instant at which the user makes a critique and the number of satisfied critiques. Moreover, we consider that the impact of features on the similarity differs according to the preferences manifested by the user. We propose a Global Weighting (GW) approach that uses a common weight for nearest cases in order to focus on groups of relevant products. We show that our methodology significantly improves recommendation efficiency in four data sets of different sizes in terms of session length in comparison with state-of-the-art approaches. Moreover, our recommender shows higher robustness against noisy user data when compared to classical approaches.
Monte Carlo methods, Recommender systems, Current measurement, Cognition, Navigation, Learning, Space exploration, personalization., Conversational recommender systems, case-based reasoning, critiquing elicitation
Maria Salamó Llorente, Sergio Escalera Guerrero, "Increasing Retrieval Quality in Conversational Recommenders", IEEE Transactions on Knowledge & Data Engineering, vol.24, no. 10, pp. 1876-1888, Oct. 2012, doi:10.1109/TKDE.2011.116
[1] D. Aha, T. Maney, and L. Breslow, "Supporting Dialogue Inferencing in Conversational Case-Based Reasoning." Proc. Fourth European Workshop Case-Based Reasoning (EWCBR '98), pp. 267-278, 1998.
[2] D. Aha, L. Breslow, and H. Muñoz-Avila, "Conversational Case-Based Reasoning," Applied Intelligence, vol. 14, pp. 9-32, 2000.
[3] B. Smyth, "Case-Based Recommendation," The Adaptive Web, P. Brusilovsky, A. Kobsa, and W. Nejdl, eds., pp. 342-376, Springer-Verlag, 2007.
[4] J.B. Schafer, D. Frankowski, J. Herlocker, and S. Sen, "Collaborative Filtering Recommender Systems," The Adaptive Web, P. Brusilovsky A. Kobsa, and W. Nejdl, eds., pp. 291-324, Springer-Verlag, 2007.
[5] M. Göker and C. Thompson, "Personalized Conversational Case-Based Recommendation," Proc. Fifth European Workshop Advances in Case-Based Reasoning (EWCBR '00), pp. 99-111, 2000.
[6] R. Burke, "Interactive Critiquing for Catalog Navigation in E-Commerce," Artificial Intelligence Rev., vol. 18, nos. 3/4, pp. 245-267, 2002.
[7] F. Ricci and N. Nguyen, "Mobyrek: A Conversational Recommender System for on-the-move Travelers," Destination Recommendation Systems: Behavioural Foundations and Applications, D.R. Fesenmaier, H. Werthner, and K.W. Wober, eds., pp. 281-294, CABI Publishing, 2006.
[8] H. Shimazu, A. Shibata, and K. Nihei, "ExpertGuide: A Conversational Case-Based Reasoning Tool for Developing Mentors in Knowledge Spaces," Applied Intelligence, vol. 14, no. 1, pp. 33-48, 2002.
[9] B. Smyth and L. McGinty, "An Analysis of Feedback Strategies in Conversational Recommender Systems." Proc. 14th Nat'l Conf. Artificial Intelligence and Cognitive Science, 2003.
[10] R. Burke, K. Hammond, and B. Young, "The FindMe Approach to Assisted Browsing," J. IEEE Expert, vol. 12, no. 4, pp. 32-40, July/Aug. 1997.
[11] L. McGinty and B. Smyth, "Tweaking Critiquing," Proc. Workshop Personalization and Web Techniques at the Int'l Joint Conf. Artificial Intelligence, 2003.
[12] D. McSherry and C. Stretch, "Automating the Discovery of Recommendation Knowledge," Proc. 19th Int'l Joint Conf. Artificial Intelligence, pp. 9-14, 2005.
[13] D. McSherry, "Increasing Dialogue Efficiency in Case-Based Reasoning without Loss of Solution Quality," Proc. 18th Int'l Joint Conf. Artificial Intelligence, pp. 121-126, 2003.
[14] L. McGinty and B. Smyth, "Comparison-Based Recommendation," Proc. Sixth European Conf. Case-Based Reasoning, pp. 575-589, 2002.
[15] H. Shimazu, "ExpertClerk: A Conversational Case-Based Reasoning Tool for Developing Salesclerk Agents in E-Commerce Webshops," Artificial Intelligence Rev., vol. 18 nos. 3/4, pp. 223-244, 2002.
[16] J. Reilly, K. McCarthy, L. McGinty, and B. Smyth, "Incremental Critiquing," Proc. Research and Development in Intelligent Systems XXI (AI '04), pp. 101-114, 2004.
[17] M. Salamó, J. Reilly, L. McGinty, and B. Smyth, "Improving Incremental Critiquing," Proc. 16th Conf. Artificial Intelligence and Cognitive Science, pp. 379-388, 2005.
[18] R.S. Sutton and A.G. Barto, Reinforcement Learning: An Introduction. The MIT Press, 1998.
[19] L. Kaelbling, M. Littman, and A. Moore, "Reinforcement Learning: A Survey," J. Artificial Intelligence Research, vol. 4, pp. 237-285, 1996.
[20] A. Moon, T. Kang, H. Kim, and H. Kim, "A Service Recommendation Using Reinforcement Learning for Network-Based Robots in Ubiquitous Computing Environments," Proc. IEEE 16th Int'l Conf. Robot and Human Interactive Comm., 2007.
[21] M. Sharma, M. Holmes, J. Santamar, A. Irani, and A. Ram, "Transfer Learning in Real-Time Strategy Games Using Hybrid CBR/RL," Proc. 20th Int'l Joint Conf. Artificial Intelligence, 2007.
[22] R. Ros, M. Veloso, R. López de Mántaras, C. Sierra, and J.L. Arcos, "Retrieving and Reusing Game Plays for Robot Soccer," Proc. Eighth European Conf. Advances in Case-Based Reasoning, pp. 47-61, 2006.
[23] H. Muñoz-Avila and J. Hullen, "Feature Weighting by Explaining Case-Based Planning Episodes," Proc. European Workshop Case-Based Reasoning, 1996.
[24] B. Auslander, S. Lee-Urban, C. Hogg, and H. Munoz-Avila, "Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning," Proc. European Conf. Case-Based Reasoning, 2008.
[25] N. Golovin and E. Rahm, "Reinforcement Learning Architecture for Web Recommendations," Proc. Int'l Conf. Information Technology: Coding and Computing, pp. 398-402, 2004.
[26] E. Gaudioso, F. Hernandez, and J.G. Boticario, "A Reinforcement Learning Approach to Achieve Unobtrusive and Interactive Recommendation Systems for Web-Based Communities," Adaptive Hypermedia and Adaptive Web-Based Systems, vol. 3137, pp. 518-541, Springer, 2004.
[27] T. Mahmood and F. Ricci, "Adapting the Interaction State Model in Conversational Recommender Systems," Proc. 10th Int'l Conf. Electronic Commerce, pp. 1-10, 2008.
[28] D.W. Aha, "Tolerating Noisy, Irrelevant and Novel Attributes in Instance-Based Learning Algorithms," Int'l J. Man-Machine Studies, vol. 36, no. 2, pp. 267-287, 1992.
[29] R. Kohavi, P. Langley, and Y. Yun, "The Utility of Feature Weighting in Nearest-Neighbour Algorithms," Proc. Ninth European Conf. Machine Learning, 1997.
[30] M. Salamó, J. Reilly, L. McGinty, and B. Smyth, "Knowledge Discovery from User Preferences in Conversational Recommendation," Proc. Ninth European Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD '05), pp. 228-239, 2005.
[31] R. Burke, K. Hammond, and B. Young, "Knowledge-Based Navigation of Complex Information Spaces," Proc. 13th Nat'l Conf. Artificial Intelligence, pp. 462-468, 1996.
[32] B. Smyth and L. McGinty, "The Power of Suggestion," Proc. Int'l Joint Conf. Artificial Intelligence, 2003.
[33] M. Friedman, "The Use of Ranks to Avoid the Assumption of Normality Implicit in the Analysis of Variance," J. Am. Statistical Assoc., vol. 32, no. 200, pp. 675-701, Dec. 1937.
[34] M. Friedman, "A Comparison of Alternative Tests of Significance for the Problem of $m$ Rankings," The Annals of Math. Statistics, vol. 11, no. 1, pp. 86-92, Mar. 1940.
[35] J. Demšar, "Statistical Comparisons of Classifiers over Multiple Data Sets," J. Machine Learning Research, vol. 7, pp. 1-30, 2006.
[36] K. McCarthy, L. McGinty, B. Smyth, and J. Reilly, "On the Evaluation of Dynamic Critiquing: A Large-Scale User Study," Proc. 20th Nat'l Conf. Artificial Intelligence, pp. 535-540, 2005.
[37] K. McCarthy, L. McGint, B. Smyth, and J. Reilly, "A Live-User Evaluation of Incremental Dynamic Critiquing," Proc. Sixth Int'l Conf. Case-Based Reasoning Research and Development, pp. 339-352, 2005.
[38] M. Salamó, B. Smyth, K. McCarthy, J. Reilly, and L. McGinty, "Reducing Critiquing Repetition in Conversational Recommendation," Proc. Ninth Workshop Multi-Agent Information Retrieval and Recommender Systems at the Int'l Joint Conf. Artificial Intelligence, pp. 55-61, 2005.
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