Towards Real-time Webpage Relevance Prediction UsingConvex Hull Based Eye-tracking Features
Nilavra Bhattacharya, Somnath Rakshit, Jacek Gwizdka
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.