Speeding up the Metabolism in E-commerce by Reinforcement Mechanism Design

TL;DR

This paper introduces a reinforcement learning framework for impression allocation in e-commerce, optimizing both short-term and long-term returns by modeling product lifecycle stages and employing innovative permutation and experience generation methods.

Contribution

It presents a novel lifecycle-based model and a reinforcement learning mechanism for impression allocation, improving long-term platform performance.

Findings

Significant improvement over baseline solutions in simulated environment

Effective long-term return optimization through lifecycle modeling

Enhanced impression allocation strategies via reinforcement learning

Abstract

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.