You can build recommendation systems that serve both users and business goals by treating ad placement as part of the generation process, letting bids influence which items appear at inference time rather than requiring model retraining.
This paper presents GEM-Rec, a recommendation system that balances user satisfaction with platform revenue by integrating ads and bids directly into generative models. Using special control tokens and a bid-aware decoding method, the system learns when to show ads from real user behavior and adjusts which ads appear based on real-time pricing, without needing to retrain the model.