Dynamic Knapsack Optimization Towards Efficient Multi-Channel Sequential Advertising
This addresses the suboptimal performance in e-commerce advertising systems by considering multiple exposures for conversions, offering a domain-specific incremental improvement.
The paper tackles the problem of optimizing sequential advertising strategies in e-commerce by formulating it as a dynamic knapsack problem, proposing a bilevel optimization framework and reinforcement learning improvements, resulting in superior cumulative revenue over state-of-the-art baselines in experiments.
In E-commerce, advertising is essential for merchants to reach their target users. The typical objective is to maximize the advertiser's cumulative revenue over a period of time under a budget constraint. In real applications, an advertisement (ad) usually needs to be exposed to the same user multiple times until the user finally contributes revenue (e.g., places an order). However, existing advertising systems mainly focus on the immediate revenue with single ad exposures, ignoring the contribution of each exposure to the final conversion, thus usually falls into suboptimal solutions. In this paper, we formulate the sequential advertising strategy optimization as a dynamic knapsack problem. We propose a theoretically guaranteed bilevel optimization framework, which significantly reduces the solution space of the original optimization space while ensuring the solution quality. To improve the exploration efficiency of reinforcement learning, we also devise an effective action space reduction approach. Extensive offline and online experiments show the superior performance of our approaches over state-of-the-art baselines in terms of cumulative revenue.