Is This Really Relevant? A Guide to Best Practice Gaze-based Relevance Prediction Research
Melanie Heck, Paulina Sonntag, Christian Becker · 2021
As eye tracking is becoming feasible on commodity devices, it provides a powerful tool for inferring users’ perceived relevance of objects. Yet the prediction quality depends on multiple parameters that have to be considered when designing the prediction model. In this paper, we review approaches to predict relevance from gaze with regard to five design issues: 1) extracting features, 2) defining the algorithm, 3) setting a prediction scope, 4) eliminating visual distractors, and 5) evaluating the system. The insights may serve as a guide to establish best practices for the design and evaluation of relevance prediction models, thus allowing for better comparability of future work. We further discuss promising fields of application that will drive future research on gaze-based relevance prediction.