Setting the retail price as a part of marketing would affect customers' cognition regarding products and affect their post-purchase behavior of review writing. To deeply understand the relationships between retail prices and reviews, this paper designs an intelligent data-driven Generate/Test Cycle using a machine learning technique to automatically discover the relationship model from a huge amount of data without a prior hypothesis. From a unique dataset, various free-form relationship models with their own structures and parameters have been discovered. By the comprehensive evaluations of candidate models, a guided map was offered to understand the relationship between dynamic retail prices and the volume/valence of reviews for different types of products. Experimental results show that 37.69% of products in our sample exhibit the following trend: When the price is increased to a certain level, the volume of reviews shifts from a decreasing trend to an increasing trend. Results also demonstrate that a linearly increasing relationship model between prices and the valence of reviews is more suitable for the low-involvement products than for the high-involvement products. In addition to the new findings, this research provides a powerful tool to assist domain experts in building relationship models for decision making in a highly efficient manner.

将零售价格设置为营销的一部分将影响客户对产品的认知，并影响其评论撰写的购买后行为。为了深入了解零售价格和评论之间的关系，本文设计了一种智能数据驱动的“生成/测试周期”，该算法使用机器学习技术自动从大量数据中发现关系模型，而无需事先假设。从一个独特的数据集中，已经发现了各种具有其自身的结构和参数的自由形式的关系模型。通过对候选模型的综合评估，提供了一个指导性地图，以了解动态零售价格与不同类型产品的评论量/价之间的关系。实验结果表明37。我们样本中69％的产品呈现以下趋势：价格上涨到一定水平时，评论量从下降趋势转变为上升趋势。结果还表明，价格和评论价之间的线性增长关系模型更适用于低参与度产品而不是高参与度产品。除了新发现外，这项研究还提供了一个强大的工具，可以协助领域专家以高效的方式建立用于决策的关系模型。结果还表明，价格和评论价之间的线性增长关系模型更适用于低参与度产品而不是高参与度产品。除了新发现外，这项研究还提供了一个强大的工具，可以协助领域专家以高效的方式建立用于决策的关系模型。结果还表明，价格和评论价之间的线性增长关系模型更适用于低参与度产品而不是高参与度产品。除了新发现外，这项研究还提供了一个强大的工具，可以协助领域专家以高效的方式建立用于决策的关系模型。