A Factor Marginal Effect Analysis Approach and Its Application in E-Commerce Search System

Abstract

Feature explanation plays an increasingly essential role in the e-commerce search platform. Most of the existing studies focus on modeling the user’s interests to estimate the click-through rate (CTR). A good e-commerce system not only needs precise ranking to inspire users’ shopping desire but also needs feature explanation to meet the demands of shop owners. The e-commerce traffic health of merchants is very important. How to effectively achieve shop owners’ multiple goals still remains as an open problem. In industrial search systems, merchants’ key demands mainly include two aspects. On the one hand, merchants want to know rule analysis of online traffic distribution, so as to help them understand the logistics of online traffic. On the other hand, they need relevant online traffic participation tools, which instruct them to participate. To address these issues, we propose a factor marginal effect analysis approach (FMEA) based on game theory, which can compute the contribution of one-dimensional features to the enhancement of online traffic. First, we use machine learning to model the business target. Then, we improve the SHAP value algorithm, which can provide clear business insights. Finally, we calculate the marginal effect of each feature on the business outcome. In this way, we provide a traffic analysis guidance method and address merchants’ participation challenges. In fact, the FMEA has been deployed in a real-world Large-Internet-Company’s App search systems and successfully serves online e-commerce service to over hundreds of millions of consumers. Our approach can guide operational decisions effectively and bring +10.05% revenue for the flow index, +7.54% for the user feedback index, and +2.46% for the service index.