Enhancing Preference-based Linear Bandits via Human Response Time
利用人类反应时间改进基于偏好的线性老虎机学习
Interactive preference learning systems infer human preferences by presenting queries as pairs of options and collecting binary choices. Although binary choices are simple and widely used, they provide limited information about preference strength. To address this, we leverage human response times, which are inversely related to preference strength, as an additional signal. We propose a computationally efficient method that combines choices and response times to estimate…