在这项工作中，我们为数字教练提供了一个新的数据集和一种计算策略，旨在指导用户练习自我附加疗法的方案。我们的框架增强了基于规则的对话代理，具有深入学习分类器，可在用户的文本响应中识别潜在的情感，以及一种深入学习的辅助检索方法，用于制作新颖，流利和善解人意的话语。我们还制作了用户可以选择与之互动的类似人类的角色。我们的目标是在虚拟疗法课程中获得高水平的参与度。我们在n = 16名参与者的非临床试验中评估了我们的框架的有效性，在五天的时间里，所有人都至少与代理商进行了四次相互作用。我们发现，与简单的基于规则的框架相比，我们的平台在同理心，用户参与度和实用性方面的评分始终高。最后，我们提供指南，以根据收到的反馈来进一步改善应用程序的设计和性能。

We apply classical statistical methods in conjunction with the state-of-the-art machine learning techniques to develop a hybrid interpretable model to analyse 454,897 online customers' behavior for a particular product category at the largest online retailer in China, that is JD. While most mere machine learning methods are plagued by the lack of interpretability in practice, our novel hybrid approach will address this practical issue by generating explainable output. This analysis involves identifying what features and characteristics have the most significant impact on customers' purchase behavior, thereby enabling us to predict future sales with a high level of accuracy, and identify the most impactful variables. Our results reveal that customers' product choice is insensitive to the promised delivery time, but this factor significantly impacts customers' order quantity. We also show that the effectiveness of various discounting methods depends on the specific product and the discount size. We identify product classes for which certain discounting approaches are more effective and provide recommendations on better use of different discounting tools. Customers' choice behavior across different product classes is mostly driven by price, and to a lesser extent, by customer demographics. The former finding asks for exercising care in deciding when and how much discount should be offered, whereas the latter identifies opportunities for personalized ads and targeted marketing. Further, to curb customers' batch ordering behavior and avoid the undesirable Bullwhip effect, JD should improve its logistics to ensure faster delivery of orders.