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 IEEE/WIC/ACM International Conferences on Web Intelli- gence and Intelligent Agent Technology, volume 1, 451–456
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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.