Conditional Potential User Mining framework via explainable surrogate models

User prediction aims to identify the user who show potential value in e-commerce platforms. Existing user prediction algorithms primarily focus on distinguishing between potential user, those likely to purchase a specific product in the future, and non-potential user, those unlikely to do so. However, these algorithms often overlook a valuable group named conditional potential user in non-potential group. Conditional potential user, initially categorized as non-potential, can convert into potential user under specific converted condition (e.g., product trial, coupon receipt, or certification attainment). Additionally, conventional user prediction models do not offer insights into the associated converted condition and provide classification results only. Furthermore, the lack of explainability in user prediction methods limits their practical utility. To tackle these challenges, this paper proposes a definitive concept for conditional potential users and introduces an innovative user prediction framework, the Conditional Potential User Mining (CPUM). Addressing the challenge of predicting conditional potential users, CPUM employs a Gold Standard Classifier to ascertain users’ intentions, thereby identifying their interest in target products under converted conditions and enabling the prediction of conditional potential users. Moreover, CPUM adopts an explainable surrogate model with an attention mechanism to boost explainability. This model is designed to mirror the decision-making capacity of the Gold Standard Classifier and provides importance explanations for different user interactions. This enables precise identification of converted conditions in conditional potential users, thus enhancing explainability. The effectiveness of this method is substantiated by experimental results derived from extensive e-commerce datasets from Alibaba and Amazon, demonstrating its cutting-edge performance and applicability. • Introduces the concept of conditional potential users in e-commerce. • Launches CPUM framework for accurate prediction of conditional potential users. • Enhances model explainability with a surrogate model and attention mechanism. • Validates CPUM’s effectiveness with Amazon and Alibaba datasets. • Offers a scalable user prediction model adaptable to various e-commerce platforms.