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- W3033684809 abstract "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." @default.
- W3033684809 created "2020-06-12" @default.
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- W3033684809 date "2020-05-30" @default.
- W3033684809 modified "2023-09-27" @default.
- W3033684809 title "Learning Efficient Representations of Mouse Movements to Predict User Attention" @default.
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