论文标题
检测用户从在线行为中退出：依赖持续时间的潜在状态模型
Detecting User Exits from Online Behavior: A Duration-Dependent Latent State Model
论文作者
论文摘要
为了引导电子商务用户进行购买，营销人员依靠对用户何时退出而无需购买的预测。以前，此类预测是基于隐藏的马尔可夫模型（HMM），因为它们具有不同用户意图的潜在购物阶段建模的能力。在这项工作中，我们开发了持续时间依赖的隐藏马尔可夫模型。与传统的HMM相反，它明确地对潜在状态的持续时间进行了建模，从而使国家变得“粘”。提出的模型在检测用户退出时优于先前的HMM：在不购买的100个用户退出中，它可以正确识别另外18个。这可以帮助营销人员更好地管理电子商务客户的在线行为。我们模型卓越性能的原因是持续时间依赖性，这使我们的模型能够恢复以扭曲时间感的特征的潜在状态。我们最终为此提供了理论上的解释，该解释基于“流”的概念。
In order to steer e-commerce users towards making a purchase, marketers rely upon predictions of when users exit without purchasing. Previously, such predictions were based upon hidden Markov models (HMMs) due to their ability of modeling latent shopping phases with different user intents. In this work, we develop a duration-dependent hidden Markov model. In contrast to traditional HMMs, it explicitly models the duration of latent states and thereby allows states to become "sticky". The proposed model is superior to prior HMMs in detecting user exits: out of 100 user exits without purchase, it correctly identifies an additional 18. This helps marketers in better managing the online behavior of e-commerce customers. The reason for the superior performance of our model is the duration dependence, which allows our model to recover latent states that are characterized by a distorted sense of time. We finally provide a theoretical explanation for this, which builds upon the concept of "flow".
