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Uplift modeling aims to estimate the treatment effect on individuals, widelyapplied in the e-commerce platform to target persuadable customers and maximizethe return of marketing activities. Among the existing uplift modeling methods,tree-based methods are adept at fitting increment and generalization, whileneural-network-based models excel at predicting absolute value and precision,and these advantages have not been fully explored and combined. Also, the lackof counterfactual sample pairs is the root challenge in uplift modeling. Inthis paper, we proposed an uplift modeling framework based on KnowledgeDistillation and Sample Matching (KDSM). The teacher model is the upliftdecision tree (UpliftDT), whose structure is exploited to constructcounterfactual sample pairs, and the pairwise incremental prediction is treatedas another objective for the student model. Under the idea of multitasklearning, the student model can achieve better performance on generalizationand even surpass the teacher. Extensive offline experiments validate theuniversality of different combinations of teachers and student models and thesuperiority of KDSM measured against the baselines. In online A/B testing, thecost of each incremental room night is reduced by 6.5\%.

KDSM: An uplift modeling framework based on knowledge distillation and sample matching