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

TL;DR

This paper explores how eye-tracking data, specifically word-eye-fixations, can be used to predict search query terms by analyzing gaze behavior during search sessions.

Contribution

It introduces a method to predict query terms using gaze data and demonstrates high accuracy with limited training data in a social sciences domain.

Findings

Gaze features significantly improve query term prediction accuracy.

Fixation, Query Relevance, and Session Topic are the most effective feature categories.

High prediction accuracy achieved with minimal training data.

Abstract

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.