Abstract
Preference elicitation (PE) is an very important component of interactive decision support systems that aim to make optimal recommendations to users by actively querying their preferences. In this paper, we present three principles important for PE in real-world problems: (1) multiattribute, (2) low cognitive load, and (3) robust to noise. In light of three requirements, we introduce an approximate PE framework based on a variant of TrueSkill for performing efficient closed-form Bayesian updates and query selection for a multiattribute utility belief state — a novel PE approach that naturally facilitates the efficient evaluation of value of information (VOI) for use in query selection strategies. Our VOI query strategy satisfies all three principles and performs on par with the most accurate algorithms on experiments with a synthetic data set.
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Guo, S., Sanner, S. (2010). Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries. In: Zhang, L., Lu, BL., Kwok, J. (eds) Advances in Neural Networks - ISNN 2010. ISNN 2010. Lecture Notes in Computer Science, vol 6063. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13278-0_51
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DOI: https://doi.org/10.1007/978-3-642-13278-0_51
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