Ad-load balancing is a critical challenge in online advertising systems,
particularly in the context of social media platforms, where the goal is to
maximize user engagement and revenue while maintaining a satisfactory user
experience. This requires the optimization of conflicting objectives, such as
user satisfaction and ads revenue. Traditional approaches to ad-load balancing
rely on static allocation policies, which fail to adapt to changing user
preferences and contextual factors. In this paper, we present an approach that
leverages off-policy learning and evaluation from logged bandit feedback. We
start by presenting a motivating analysis of the ad-load balancing problem,
highlighting the conflicting objectives between user satisfaction and ads
revenue. We emphasize the nuances that arise due to user heterogeneity and the
dependence on the user's position within a session. Based on this analysis, we
define the problem as determining the optimal ad-load for a particular feed
fetch. To tackle this problem, we propose an off-policy learning framework that
leverages unbiased estimators such as Inverse Propensity Scoring (IPS) and
Doubly Robust (DR) to learn and estimate the policy values using offline
collected stochastic data. We present insights from online A/B experiments
deployed at scale across over 80 million users generating over 200 million
sessions, where we find statistically significant improvements in both user
satisfaction metrics and ads revenue for the platform.

在线广告平台中，通过应用离线数据采集的反例权重评估方法和双重保守法，以最大程度提高用户满意度和广告收入为目标，解决了广告负载平衡问题。在超过 8000 万用户和 2 亿次会话的实施规模的在线 A/B 测试中，发现用户满意度指标和广告收入都有显著的提高。