Towards Real-time Webpage Relevance Prediction UsingConvex Hull Based Eye-tracking Features

Abstract

Browsing the web for finding answers to questions has become pervasive in our everyday lives. When users search the web to satisfy their information-needs, their on-screen eye movements can serve as a source of implicit relevance feedback. We analyze data collected from two eye-tracking studies, wherein participants read online news-articles, and judged whether they contained answers to factual questions. We propose two eye-tracking features, derived from the area of the convex hull of their eye fixations. We demonstrate that these features can well distinguish between eye-movements on news-articles perceived to be relevant vs. irrelevant, for containing the answer to a question. These features can potentially be used for predicting the user’s perceived-relevance in real-time. F1 scores as high as 0.80 are obtained using these proposed features only, and the performance is comparable to the combined predictive power of fifteen eye-tracking features established by prior literature.