A Recurrent Neural Network Survival Model: Predicting Web User Return Time
A Recurrent Neural Network Survival Model: Predicting Web User Return Time
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DOI：
10.1007/978-3-030-10997-4_10
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发表时间：
2018-07
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影响因子：
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通讯作者：
Georg L. Grob;Ângelo Cardoso;C. H. B. Liu;Duncan A. Little;B. Chamberlain
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文献类型：
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作者：
Georg L. Grob;Ângelo Cardoso;C. H. B. Liu;Duncan A. Little;B. Chamberlain
一个网站的活跃用户群的大小直接影响其价值。因此，监控和影响用户返回站点的可能性是重要的。关键是预测用户何时返回。解决这个问题的现有技术方法有两种：（1）基于递归神经网络（RNN）的解决方案和（2）生存分析方法。我们观察到，这两种技术是严重限制时，适用于这个问题。生存模型只能合并用户的聚合表示，而不是直接从用户操作的原始时间序列自动学习表示。RNN可以自动学习特征，但不能直接用没有返回时间目标值的非返回用户的例子进行训练。我们开发了一种新的RNN生存模型，消除了现有方法的局限性。我们证明，该模型可以成功地应用于返回时间预测的一个大型电子商务数据集，具有上级的能力，区分返回和非返回的用户比任何一种方法单独应用。与本文相关的代码可在以下网址获得： https://github.com/grobgl/rnnsm .
The size of a website’s active user base directly affects its value. Thus, it is important to monitor and influence a user’s likelihood to return to a site. Essential to this is predictingwhena user will return. Current state of the art approaches to solve this problem come in two flavors: (1) Recurrent Neural Network (RNN) based solutions and (2) survival analysis methods. We observe that both techniques are severely limited when applied to this problem. Survival models can only incorporate aggregate representations of users instead of automatically learning a representation directly from a raw time series of user actions. RNNs can automatically learn features, but can not be directly trained with examples of non-returning users who have no target value for their return time. We develop a novel RNN survival model that removes the limitations of the state of the art methods. We demonstrate that this model can successfully be applied to return time prediction on a large e-commerce dataset with a superior ability to discriminate between returning and non-returning users than either method applied in isolation. Code related to this paper is available at: https://github.com/grobgl/rnnsm .