Ads Supply Personalization via Doubly Robust Learning
This work addresses ads supply personalization for social media platforms, offering a scalable solution with demonstrated business impact, though it appears incremental as it builds on existing doubly robust methods.
The paper tackles the problem of balancing revenue and user engagement in social media ads by personalizing ad supply, using a doubly robust learning framework that improves long-term treatment effect estimates and has been deployed on a major platform, showing significant improvements in business metrics over months.
Ads supply personalization aims to balance the revenue and user engagement, two long-term objectives in social media ads, by tailoring the ad quantity and density. In the industry-scale system, the challenge for ads supply lies in modeling the counterfactual effects of a conservative supply treatment (e.g., a small density change) over an extended duration. In this paper, we present a streamlined framework for personalized ad supply. This framework optimally utilizes information from data collection policies through the doubly robust learning. Consequently, it significantly improves the accuracy of long-term treatment effect estimates. Additionally, its low-complexity design not only results in computational cost savings compared to existing methods, but also makes it scalable for billion-scale applications. Through both offline experiments and online production tests, the framework consistently demonstrated significant improvements in top-line business metrics over months. The framework has been fully deployed to live traffic in one of the world's largest social media platforms.