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Learning Efficient Representations of Mouse Movements to Predict User Attention
2020
arXiv
pre-print
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
arXiv:2006.01644v1
fatcat:5qqjysljwbfcbcqao3oh3yavb4