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.
Original language | English |
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Pages (from-to) | 289-296 |
Number of pages | 8 |
Journal | Journal of Machine Learning Research |
Volume | 9 |
Publication status | Published - 2010 |
Event | 13th International Conference on Artificial Intelligence and Statistics, AISTATS 2010 - Sardinia, Italy Duration: 13 May 2010 → 15 May 2010 |