  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.
