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Tracking mouse cursor movements can be used to predict user attention onheterogeneous page layouts like SERPs. So far, previous work has relied heavilyon handcrafted features, which is a time-consuming approach that often requiresdomain expertise. We investigate different representations of mouse cursormovements, including time series, heatmaps, and trajectory-based images, tobuild and contrast both recurrent and convolutional neural networks that canpredict user attention to direct displays, such as SERP advertisements. Ourmodels are trained over raw mouse cursor data and achieve competitiveperformance. We conclude that neural network models should be adopted fordownstream tasks involving mouse cursor movements, since they can provide aninvaluable implicit feedback signal for re-ranking and evaluation.

Learning Efficient Representations of Mouse Movements to Predict User Attention