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Issue No. 10 - Oct. (2012 vol. 24)
ISSN: 1041-4347
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. , pp. 1876-1888, Oct. 2012, doi:10.1109/TKDE.2011.116
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