PupilMeter: Modeling User Preference with Time-Series Features of Pupillary Response

Abstract

Modeling user preferences is a challenging problem in the wide application of recommendation services. Existing methods mainly exploit multiple activities irrelevant to user's inner feeling to build user preference model, which may raise model uncertainty and bring about prediction error. In this paper, we present PupilMeter - the first system that moves one step forward towards exploring the correlation between user preference and the instant pupillary response. Specifically, we conduct extensive experiments to dig into the generic physiological process of pupillary response while viewing specific content on smart devices, and further figure out six key time-series features relevant to users' preference degree by using Random Forest. However, the diversity of pupillary responses caused by inherent individual difference poses significant challenges to the generality of learned model. To solve this problem, we use Multilayer Perceptron to automatically train and adjust the importance of key features for each individual and then generate a personalized user preference model associated with user's pupillary response. We have prototyped PupilMeter and conducted both test experiments and in-the-wild studies to comprehensively evaluate the effectiveness of PupilMeter by recruiting 30 volunteers. Experimental results demonstrate that PupilMeter can accurately identify users' preference.