A utility-based active sampling strategy for pairwise preference learning picks the questions that most improve expected recommendation quality, outperforming random and uncertainty-based baselines in two experiments.
In 2011 49th Annual Aller- ton Conference on Communication, Control, and Computing (Allerton), 1143–1150
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Personalized Recommendations via Active Utility-based Pairwise Sampling
A utility-based active sampling strategy for pairwise preference learning picks the questions that most improve expected recommendation quality, outperforming random and uncertainty-based baselines in two experiments.