论文标题
通过无内存实验利用客户终身价值
Exploit Customer Life-time Value with Memoryless Experiments
论文作者
论文摘要
为了衡量客户在服务或产品关系，终身价值或LTV中产生的长期贡献，可以更全面地找到服务交付的最佳策略。但是，准确地抽象LTV场景，合理地对其进行建模并找到最佳解决方案是一项挑战。当前的理论要么由于单个建模结构无法精确表达LTV，要么没有有效的解决方案。我们提出了一种通用的LTV建模方法，该方法解决了一个问题，即客户的长期贡献很难在现有方法（例如对点击率建模的现有方法）中进行量化，而仅追求短期贡献。同时，我们还提出了一种基于突变的一分配方法和无内存重复实验假设的快速动态编程解决方案。该模型和方法可以应用于不同的服务方案，例如推荐系统。现实世界数据集的实验证实了所提出的模型和优化方法的有效性。此外，整个LTV结构是在大型的电子商务手机应用程序中部署的，它设法选择了最佳推送消息发送时间并实现了10 \％LTV的改进。
As a measure of the long-term contribution produced by customers in a service or product relationship, life-time value, or LTV, can more comprehensively find the optimal strategy for service delivery. However, it is challenging to accurately abstract the LTV scene, model it reasonably, and find the optimal solution. The current theories either cannot precisely express LTV because of the single modeling structure, or there is no efficient solution. We propose a general LTV modeling method, which solves the problem that customers' long-term contribution is difficult to quantify while existing methods, such as modeling the click-through rate, only pursue the short-term contribution. At the same time, we also propose a fast dynamic programming solution based on a mutated bisection method and the memoryless repeated experiments assumption. The model and method can be applied to different service scenarios, such as the recommendation system. Experiments on real-world datasets confirm the effectiveness of the proposed model and optimization method. In addition, this whole LTV structure was deployed at a large E-commerce mobile phone application, where it managed to select optimal push message sending time and achieved a 10\% LTV improvement.
