Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design
This work addresses the need for long-term optimization in e-commerce platforms to enhance participant and platform health, though it appears incremental by building on existing reinforcement learning methods.
The paper tackled the problem of impression allocation in e-commerce by proposing a reinforcement learning-based mechanism design framework that maximizes both short-term and long-term returns, achieving significant improvement over baseline solutions in a simulated environment.
In a large E-commerce platform, all the participants compete for impressions under the allocation mechanism of the platform. Existing methods mainly focus on the short-term return based on the current observations instead of the long-term return. In this paper, we formally establish the lifecycle model for products, by defining the introduction, growth, maturity and decline stages and their transitions throughout the whole life period. Based on such model, we further propose a reinforcement learning based mechanism design framework for impression allocation, which incorporates the first principal component based permutation and the novel experiences generation method, to maximize short-term as well as long-term return of the platform. With the power of trial-and-error, it is possible to optimize impression allocation strategies globally which is contribute to the healthy development of participants and the platform itself. We evaluate our algorithm on a simulated environment built based on one of the largest E-commerce platforms, and a significant improvement has been achieved in comparison with the baseline solutions.