非契约异构生存数据的非参数层次贝叶斯建模

Shouichi Nagano, Yusuke Ichikawa, Noriko Takaya, Tadasu Uchiyama, M. Abe
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引用次数: 3

摘要

发现顾客生命周期并评估顾客特征变量对顾客生命周期的影响是非契约式营销领域的一个重要问题。遗憾的是,传统的层次贝叶斯模型不能识别顾客特征变量对每个顾客的影响。为了克服这个问题,我们提出了一个新的生存模型,使用非参数贝叶斯范式与MCMC。将传统模型的假设,即购买率和辍学率的对数线性回归,扩展为包含我们的Dirichlet过程混合回归的假设。扩展假设每个客户在概率上属于不同的回归混合,从而允许我们估计每个客户的客户特征变量的不同影响。我们的模型创建了几个客户组来反映目标数据集的结构。通过一个真实的电子商务交易数据集和一个人工数据集的比较,验证了本文建议的有效性;它通常可以实现更高的预测性能。此外,我们表明,预先选择客户群体的实际数量并不总是导致更高的预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Nonparametric hierarchal bayesian modeling in non-contractual heterogeneous survival data
An important problem in the non-contractual marketing domain is discovering the customer lifetime and assessing the impact of customer's characteristic variables on the lifetime. Unfortunately, the conventional hierarchical Bayes model cannot discern the impact of customer's characteristic variables for each customer. To overcome this problem, we present a new survival model using a non-parametric Bayes paradigm with MCMC. The assumption of a conventional model, logarithm of purchase rate and dropout rate with linear regression, is extended to include our assumption of the Dirichlet Process Mixture of regression. The extension assumes that each customer belongs probabilistically to different mixtures of regression, thereby permitting us to estimate a different impact of customer characteristic variables for each customer. Our model creates several customer groups to mirror the structure of the target data set. The effectiveness of our proposal is confirmed by a comparison involving a real e-commerce transaction dataset and an artificial dataset; it generally achieves higher predictive performance. In addition, we show that preselecting the actual number of customer groups does not always lead to higher predictive performance.
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