A contemporary feed application usually provides blended results of organic
items and sponsored items~(ads) to users. Conventionally, ads are exposed at
fixed positions. Such a static exposure strategy is inefficient due to ignoring
users' personalized preferences towards ads. To this end, adaptive ad exposure
has become an appealing strategy to boost the overall performance of the feed.
However, existing approaches to implementing the adaptive ad exposure still
suffer from several limitations: 1) they usually fall into sub-optimal
solutions because of only focusing on request-level optimization without
consideration of the long-term application-level performance and constraints,
2) they neglect the necessity of keeping the game-theoretical properties of ad
auctions, which may lead to anarchy in bidding, and 3) they can hardly be
deployed in large-scale applications due to high computational complexity. In
this paper, we focus on long-term performance optimization under hierarchical
constraints in feeds and formulate the adaptive ad exposure as a Dynamic
Knapsack Problem. We propose an effective approach: Hierarchically Constrained
Adaptive Ad Exposure~(HCA2E). We present that HCA2E possesses desired
game-theoretical properties, computational efficiency, and performance
robustness. Comprehensive offline and online experiments on a leading
e-commerce application demonstrate the significant performance superiority of
HCA2E over representative baselines. HCA2E has also been deployed on this
application to serve millions of daily users.

本文针对现代推荐系统中广告内容的静态展示策略效果低下的问题，提出了一种适应性广告展示策略，并将其作为一种动态背包问题优化，考虑到了长期的应用层面性能和限制，达到了很好的效果。

在 Feed 中实施分层约束自适应广告曝光

Hierarchically Constrained Adaptive Ad Exposure in Feeds

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.

本研究将顺序广告策略优化建模为动态背包问题，并提出了一种具有理论保证的二层优化框架，以显著降低原始优化空间的解决方案，同时确保解决方案的质量，进一步地通过缩小行动空间提高了增强学习的探索效率。离线和在线实验表明，相比工艺水平的基线方法，我们的方法在累计收入方面表现优异。