  Tracking mouse cursor movements can be used to predict user attention on
heterogeneous page layouts like SERPs. So far, previous work has relied heavily
on handcrafted features, which is a time-consuming approach that often requires
domain expertise. We investigate different representations of mouse cursor
movements, including time series, heatmaps, and trajectory-based images, to
build and contrast both recurrent and convolutional neural networks that can
predict user attention to direct displays, such as SERP advertisements. Our
models are trained over raw mouse cursor data and achieve competitive
performance. We conclude that neural network models should be adopted for
downstream tasks involving mouse cursor movements, since they can provide an
invaluable implicit feedback signal for re-ranking and evaluation.
