PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Real-time Multiattribute Bayesian Preference Elicitation with Pairwise Comparison Queries
Shengbo Guo and Scott Sanner
In: The 13th International Conference on Artificial Intelligence and Statistics, 13-15 May 2010, Chia Laguna Resort, Sardinia, Italy.

Abstract

Preference elicitation (PE) is an important component of interactive decision support systems that aim to make optimal recommendations to users by actively querying their preferences. In this paper, we outline five principles important for PE in real-world problems: (1) real-time, (2) multiattribute, (3) low cognitive load, (4) robust to noise, and (5) scalable. In light of these requirements, we introduce an approximate PE framework based on 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) heuristics for use in query selection strategies. Our best VOI query strategy satisfies all five principles (in contrast to related work) and performs on par with the most accurate (and often computationally intensive) algorithms on experiments with synthetic and real-world datasets.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:6986
Deposited By:Shengbo Guo
Deposited On:12 August 2010