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Generating a predictive behavior model for predicting user behavior using unsupervised feature learning and a recurrent neural network
Generating a predictive behavior model for predicting user behavior using unsupervised feature learning and a recurrent neural network
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机译:生成预测性行为模型,用于预测用户行为使用无监督特征学习和经常性神经网络
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摘要
Certain embodiments involve a model for predicting user behavior. For example, a system accesses user behavior data indicating various users' behaviors during intervals over various periods of time and target behavior data indicating a particular user behavior. The system associates each user with a label that indicates whether a user performed a particular action during or after a time period based on the target behavior data. The system uses the user behavior data to train various deep Restricted Boltzmann Machines (“RBM”) to generate representations of each user over each period of time that indicate the user behavior over the time period. The system generates a predictive model by connecting the RBMs into a deep recurrent neural network and uses the target behavior data associated with each user, along with the representations of each user, as input data to train the deep recurrent neural network to predict user behavior.
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