流失预测的反事实建模框架

Guozhen Zhang, Jinwei Zeng, Zhengyue Zhao, Depeng Jin, Yong Li
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引用次数: 4

摘要

准确的用户流失预测对于在线服务来说非常重要,因为这决定了他们的生存和繁荣。最近的研究指出,社会影响是导致用户流失的最重要原因之一,因此许多研究开始建立社会影响对用户流失的影响模型,以提高预测性能。然而,现有的研究只利用了数据的相关信息,而忽略了问题的因果性。具体来说,虽然用户流失与某些社交因素相关,但这并不意味着他/她实际上受到了他/她的朋友的影响,这导致了现有方法的预测不准确和无法解释。为了弥补这一差距,我们开发了一个反事实模型框架,用于流失预测,该框架可以有效地捕捉社会影响的因果信息,以准确和可解释的流失预测。具体而言,我们首先提出了一个骨干框架,该框架使用两个独立的嵌入来模拟用户的内生流失意图和外生社会影响。然后,我们提出了一个反事实数据增强模块,通过提供部分标记的反事实数据,将因果信息引入模型。最后,我们设计了一个三头反事实预测框架来指导模型学习因果信息,以促进流失预测。在两个具有不同类型社会关系的大型数据集上进行的大量实验表明,与最先进的基线相比,我们的模型具有优越的预测性能。我们进一步对预测结果进行了深入分析,证明我们提出的方法能够捕捉社会影响的因果信息,并给出可解释的流失预测,这为设计更好的用户留存策略提供了见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Counterfactual Modeling Framework for Churn Prediction
Accurate churn prediction for retaining users is keenly important for online services because it determines their survival and prosperity. Recent research has specified social influence to be one of the most important reasons for user churn, and thereby many works start to model its effects on user churn to improve the prediction performance. However, existing works only use the data's correlational information while neglecting the problem's causal nature. Specifically, the fact that a user's churn is correlated with some social factors does not mean he/she is actually influenced by his/her friends, which results in inaccurate and unexplainable predictions of the existing methods. To bridge this gap, we develop a counterfactual modeling framework for churn prediction, which can effectively capture the causal information of social influence for accurate and explainable churn predictions. Specifically, we first propose a backbone framework that uses two separate embeddings to model users' endogenous churn intentions and the exogenous social influence. Then, we propose a counterfactual data augmentation module to introduce the causal information to the model by providing partially labeled counterfactual data. Finally, we design a three-headed counterfactual prediction framework to guide the model to learn causal information to facilitate churn prediction. Extensive experiments on two large-scale datasets with different types of social relations show our model's superior prediction performance compared with the state-of-the-art baselines. We further conduct an in-depth analysis of the prediction results demonstrating our proposed method's ability to capture causal information of social influence and give explainable churn predictions, which provide insights into designing better user retention strategies.
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