Learning to Allocate Incentives for Incentivized Advertising via Offline Model-Based Reinforcement Learning
Artificial Intelligence
Summary
The authors study how to set bonuses for users who watch ads, balancing enough reward to encourage clicks without losing profit. They treat this as a step-by-step decision problem where early incentives affect future user behavior and revenue. Using a type of reinforcement learning, they build a model that predicts user responses and ad earnings, then choose the best strategy without expensive real-world tests. Their experiments show this method improves profit compared to previous approaches.
Authors
Zilin Zhao, Han Yang, Tianpei Yang, Fangsheng Huang, Yanfei Cui, Kan Peng, Yi Li, Yiming Zong, Hao Zhang, Yinsong Xue
Abstract
Complete your ad view and grab a 5-cent bonus! In incentivized advertising, a platform promises users a bonus before observing downstream ad revenue, encouraging them to click and complete ads. It must balance the incentive promised in advance against the revenue realized afterward: insufficient incentives forfeit monetization opportunities, whereas excessive incentives reduce net profit. Because current incentives may also shape user expectations and future engagement, incentive allocation is a sequential decision problem with delayed revenue, cost sensitivity, and carryover effects. Existing work has not studied decision-making algorithms for this setting. Auto-bidding assumes available ad opportunities, while targeted promotion optimizes incentives outside the ad monetization pipeline. We formulate the problem as an MDP and develop an offline model-based RL framework for cost-controllable sequential incentive allocation. It learns a world model of user feedback and ad revenue, then performs conservative policy optimization. An independent counterfactual scorer evaluates each learned policy on held-out logs, enabling pre-launch selection without costly online exposure. Experiments on large-scale industrial data and online A/B tests show that the scorer provides a stable offline signal. The deployment path from causal inference to offline RL and then Offline-MBRL further validates the framework: MB-IQL improves per-user net profit by 7.96\% over TD3+BC, whereas reverting to plain IQL reduces it by 6.56\% (both \(p<0.0001\)).