Learning from Double Positive and Unlabeled Data for Potential-Customer Identification
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利用双重正例与未标记数据学习识别潜在客户
- 研究问题
- 当企业只能观察已购买者时，如何从有限的正例与未标记数据中识别对产品有兴趣、但对企业忠诚度不高的潜在营销对象？
- 方法与思路
- 作者提出双重正例与未标记数据学习，在单阶段优化目标中隐含两项标准此类学习损失，分别服务于兴趣与忠诚度的区分。
- 材料陈述的贡献
- 摘要称，该方法能够针对兴趣和忠诚度两个条件构造潜在客户分类器，并通过数值实验检验其在所设问题中的运行表现。
- 阅读关注点
- 应核对购买、兴趣和忠诚度之间的观测机制，检查类别比例或标签生成要求，以及两项损失的构造和实验标签如何获得。
- 阅读边界
- 这是 arXiv 预印本，同行评审或接收未核实；摘要未说明分类识别条件和误差数值，数值实验不能证明实际营销的增量效果。
原文摘要
In this study, we propose a method for identifying potential customers in targeted marketing by applying learning from positive and unlabeled data (PU learning). We consider a scenario in which a company sells a product and can observe only the customers who purchased it. Decision-makers seek to market products effectively based on whether people have loyalty to the company. Individuals with loyalty are those who are likely to remain interested in the company even without additional advertising. Consequently, those loyal customers would likely purchase from the company if they are interested in the product. In contrast, people with lower loyalty may overlook the product or buy similar products from other companies unless they receive marketing attention. Therefore, by focusing marketing efforts on individuals who are interested in the product but do not have strong loyalty, we can achieve more efficient marketing. To achieve this goal, we consider how to learn, from limited data, a classifier that identifies potential customers who (i) have interest in the product and (ii) do not have loyalty to the company. Although our algorithm comprises a single-stage optimization, its objective function implicitly contains two losses derived from standard PU learning settings. For this reason, we refer to our approach as double PU learning. We verify the validity of the proposed algorithm through numerical experiments, confirming that it functions appropriately for the problem at hand.