A practical query selection framework for real-time Bayesian preference elicitation

Bayesian Preference Elicitation (PE) is an active learning technique aimed at discovering users’ preferences through a suitable sequence of queries. One of its prominent applications consists in providing personalized product recommendations in e-Commerce scenarios. In these settings, where catalogs may contain hundreds or thousands of different products, it becomes crucial to find a good trade-off between recommendation quality and computational cost.In this paper we introduce QUEST, a highly configurable PE framework supporting arbitrary value functions on multi-attribute product domains. We use QUEST to compare established and novel query selection methodologies to achieve real-time user interaction, while preserving recommendation quality. In particular, the experimental assessment shows that a novel uncertainty-based query selection strategy outperforms the largely used value-of-information (VOI) both in accuracy and execution time.