Causal-Informed Hybrid Online Adaptive Optimization for Ad Load Personalization in Large-Scale Social Networks
Combines constraint-safe primal-dual control with trust-region Bayesian optimization, guided by a causal model, to personalize how many ads people see. Tested in live A/B tests on a billion-user social network.
Abstract
Personalizing ad load in large-scale social networks requires balancing user experience and conversions under operational constraints. Traditional primal-dual methods enforce constraints reliably but adapt slowly in dynamic environments, while Bayesian Optimization (BO) enables exploration but suffers from slow convergence.
We propose a hybrid online adaptive optimization framework, CTRCBO (Cohort-Based Trust Region Contextual Bayesian Optimization), combining primal-dual with BO, enhanced by trust-region updates and Gaussian Process Regression (GPR) surrogates for both objectives and constraints. Our approach leverages an upstream causal ML model to inform the surrogate, improving decision quality and enabling efficient exploration-exploitation and online tuning.
We evaluate our method on a billion-user social network, demonstrating faster convergence, robust constraint satisfaction, and improved personalization metrics, including real-world online A/B test results.
Cite
@inproceedings{mishra2025causal,
title = {Causal-Informed Hybrid Online Adaptive Optimization for Ad Load Personalization in Large-Scale Social Networks},
author = {Aakash Mishra and Qi Xu and Zhigang Hua and Keyu Nie and Vishwanath Sangale and Vishal Vaingankar and Jizhe Zhang and Ren Mao},
year = {2025},
booktitle = {NeurIPS 2025 Workshop on Constrained Optimization for Machine Learning},
eprint = {2602.10129},
archivePrefix = {arXiv}
}