AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising

Researchers propose AHBid, a new hierarchical bidding framework for cross-channel advertising that combines generative planning with real-time control using diffusion models. The system achieved a 13.57% improvement in return on investment compared to existing methods in large-scale tests.

• →AHBid integrates generative planning with real-time control to optimize advertising bids across multiple channels
• →The framework uses diffusion models to dynamically allocate budgets while capturing historical context and temporal patterns
• →System includes constraint enforcement and trajectory refinement mechanisms for better adaptability to market changes
• →Extensive testing shows 13.57% increase in overall return compared to existing baseline methods
• →Framework addresses limitations of both optimization-based and reinforcement learning approaches in advertising