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Throughout the search process, the user's gaze on inspected SERPs andwebsites can reveal his or her search interests. Gaze behavior can be capturedwith eye tracking and described with word-eye-fixations. Word-eye-fixationscontain the user's accumulated gaze fixation duration on each individual wordof a web page. In this work, we analyze the role of word-eye-fixations forpredicting query terms. We investigate the relationship between a range ofin-session features, in particular, gaze data, with the query terms and trainmodels for predicting query terms. We use a dataset of 50 search sessionsobtained through a lab study in the social sciences domain. Using establishedmachine learning models, we can predict query terms with comparably highaccuracy, even with only little training data. Feature analysis shows that thecategories Fixation, Query Relevance and Session Topic contain the mosteffective features for our task.

The Role of Word-Eye-Fixations for Query Term Prediction