Comparative preferences induction methods for conversational recommenders

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Trabelsi, Walid
Wilson, Nic
Bridge, Derek G.
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Springer Berlin Heidelberg
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In an era of overwhelming choices, recommender systems aim at recommending the most suitable items to the user. Preference handling is one of the core issues in the design of recommender systems and so it is important for them to catch and model the user’s preferences as accurately as possible. In previous work, comparative preferences-based patterns were developed to handle preferences deduced by the system. These patterns assume there are only two values for each feature. However, real-world features can be multi-valued. In this paper, we develop preference induction methods which aim at capturing several preference nuances from the user feedback when features have more than two values. We prove the efficiency of the proposed methods through an experimental study.
Programming techniques , Computer communication networks , Probability and statistics in computer science , Algorithm analysis and problem complexity , Artificial intelligence (incl. robotics) , Information systems applications (incl. internet)
TRABELSI, W., WILSON, N. & BRIDGE, D. G. 2013. Comparative preferences induction methods for conversational recommenders. In: PERNEY, P., PIRLOT, M. & TSOUKIÀS, A. (eds.) Algorithmic Decision Theory. Bruxelles, Belgium, 13-15 Nov. Berlin Heidelberg: Springer, pp. 363-374
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