论文标题
学习鼠标运动的有效表示以预测用户注意
Learning Efficient Representations of Mouse Movements to Predict User Attention
论文作者
论文摘要
跟踪鼠标光标运动可用于预测用户对SERP等异质页面布局的关注。到目前为止，以前的工作已严重依赖手工制作的功能，这是一种耗时的方法，通常需要域专业知识。我们研究了鼠标光标运动的不同表示，包括时间序列，热图和基于轨迹的图像，以构建和对比复发和卷积神经网络，这些神经网络可以预测用户对直接显示的关注，例如SERP广告。我们的模型经过原始鼠标光标数据的培训并实现竞争性能。我们得出的结论是，应采用涉及鼠标光标运动的下游任务的神经网络模型，因为它们可以为重新排列和评估提供宝贵的隐式反馈信号。
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.
