# Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks Source: [Relevance Prediction from Eye-movements Using Semi-interpretable Convolutional Neural Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:02:19.923Z Authors: Nilavra Bhattacharya, Somnath Rakshit, Jacek Gwizdka, Paul Kogut Published: 2020-01-15T07:02:14.000Z ## Abstract We propose an image-classification method to predict the perceived-relevance of text documents from eye-movements. An eye-tracking study was conducted where participants read short news articles, and rated them as relevant or irrelevant for answering a trigger question. We encode participants' eye-movement scanpaths as images, and then train a convolutional neural network classifier using these scanpath images. The trained classifier is used to predict participants' perceived-relevance of news articles from the corresponding scanpath images. This method is content-independent, as the classifier does not require knowledge of the screen-content, or the user's information-task. Even with little data, the image classifier can predict perceived-relevance with up to 80% accuracy. When compared to similar eye-tracking studies from the literature, this scanpath image classification method outperforms previously reported metrics by appreciable margins. We also attempt to interpret how the image classifier differentiates between scanpaths on relevant and irrelevant documents. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()