  World-wide-web, with the website and webpage as the main interface,
facilitates the dissemination of important information. Hence it is crucial to
optimize them for better user interaction, which is primarily done by analyzing
users' behavior, especially users' eye-gaze locations. However, gathering these
data is still considered to be labor and time intensive. In this work, we
enable the development of automatic eye-gaze estimations given a website
screenshots as the input. This is done by the curation of a unified dataset
that consists of website screenshots, eye-gaze heatmap and website's layout
information in the form of image and text masks. Our pre-processed dataset
allows us to propose an effective deep learning-based model that leverages both
image and text spatial location, which is combined through attention mechanism
for effective eye-gaze prediction. In our experiment, we show the benefit of
careful fine-tuning using our unified dataset to improve the accuracy of
eye-gaze predictions. We further observe the capability of our model to focus
on the targeted areas (images and text) to achieve high accuracy. Finally, the
comparison with other alternatives shows the state-of-the-art result of our
model establishing the benchmark for the eye-gaze prediction task.
