Predicting eye-tracking assisted web page segmentation

Different kinds of algorithms have been proposed to identify the visual elements of web pages for different purposes, such as improving web accessibility, measuring web page visual quality and aesthetics etc. One group of these algorithms identifies the elements by analyzing the source code and visual representation of web pages, whereas another group discovers the attractive elements by analyzing the eye movements of users. A previous approach proposes combining these two approaches to consider both the source code and visual representation of web pages and users’ eye movements on those pages. The result of the proposed approach can be considered eye-tracking-assisted web page segmentation. However, since the eye-tracking data collection procedure is elaborate, time-consuming, and expensive, and it is not feasible to collect eye-tracking data for each page, we aim to develop a model to predict such segmentation without requiring eye-tracking data. In this paper, we present our experiments with different Machine and Deep Learning algorithms and show that the K-Nearest Neighbour (KNN) model yields the best results in prediction. We present a KNN model that predicts eye-tracking-assisted web page segmentation with an F1-score of 78.74%. This work shows how an Machine Learning algorithm can automate web page segmentation driven by eye-tracking data.