  Throughout the search process, the user's gaze on inspected SERPs and
websites can reveal his or her search interests. Gaze behavior can be captured
with eye tracking and described with word-eye-fixations. Word-eye-fixations
contain the user's accumulated gaze fixation duration on each individual word
of a web page. In this work, we analyze the role of word-eye-fixations for
predicting query terms. We investigate the relationship between a range of
in-session features, in particular, gaze data, with the query terms and train
models for predicting query terms. We use a dataset of 50 search sessions
obtained through a lab study in the social sciences domain. Using established
machine learning models, we can predict query terms with comparably high
accuracy, even with only little training data. Feature analysis shows that the
categories Fixation, Query Relevance and Session Topic contain the most
effective features for our task.
