A Multi-stage Framework for Online Bonus Allocation Based on Constrained User Intent Detection

With the explosive development of e-commerce for service, tens of millions of orders are generated every day on the Meituan platform. By allocating bonuses to new customers when they pay, the Meituan platform encourages them to use its own payment service for a better experience in the future. It can be formulated as a multi-choice knapsack problem (MCKP), and the mainstream solution is usually a two-stage method. The first stage is user intent detection, predicting the effect for each bonus treatment. Then, it serves as the objective of the MCKP, and the problem is solved in the second stage to obtain the optimal allocation strategy. However, this solution usually faces the following challenges: (1) In the user intent detection stage, due to the sparsity of interaction and noise, the traditional multi-treatment effect estimation methods lack interpretability, which may violate the domain knowledge that the marginal gain is non-negative with the increase of the bonus amount in economic theory. (2) There is an optimality gap between the two stages, which limits the upper bound of the optimal value obtained in the second stage. (3) Due to changes in the distribution of orders online, the actual cost consumption often violates the given budget limit. To solve the above challenges, we propose a framework that consists of three modules, i.e., User Intent Detection Module, Online Allocation Module, and Feedback Control Module. In the User Intent Detection Module, we implicitly model the treatment increment based on deep representation learning and constrain it to be non-negative to achieve monotonicity constraints. Then, in order to reduce the optimality gap, we further propose a convex constrained model to increase the upper bound of the optimal value. For the third challenge, to cope with the fluctuation of online bonus consumption, we leverage a feedback control strategy in the framework to make the actual cost more accurately approach the given budget limit. Finally, we conduct extensive offline and online experiments, demonstrating the superiority of our proposed framework, which reduced customer acquisition costs by 5.07% and is still running online.