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[2602.22650] AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising
Summary
The paper presents AHBid, a novel hierarchical bidding framework for cross-channel advertising that enhances budget allocation and adaptability using generative planning and real-time control.
Why It Matters
AHBid addresses the limitations of existing bidding strategies in online advertising, particularly in multi-channel environments. By integrating historical data with real-time information, it offers a more flexible and effective solution for advertisers, potentially improving return on investment significantly.
Key Takeaways
- AHBid integrates generative planning with real-time control for better bid optimization.
- The framework enhances adaptability by capturing historical context and temporal patterns.
- AHBid demonstrates a 13.57% increase in return on investment compared to existing methods.
Computer Science > Artificial Intelligence arXiv:2602.22650 (cs) [Submitted on 26 Feb 2026] Title:AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising Authors:Xinxin Yang, Yangyang Tang, Yikun Zhou, Yaolei Liu, Yun Li, Bo Yang View a PDF of the paper titled AHBid: An Adaptable Hierarchical Bidding Framework for Cross-Channel Advertising, by Xinxin Yang and 4 other authors View PDF HTML (experimental) Abstract:In online advertising, the inherent complexity and dynamic nature of advertising environments necessitate the use of auto-bidding services to assist advertisers in bid optimization. This complexity is further compounded in multi-channel scenarios, where effective allocation of budgets and constraints across channels with distinct behavioral patterns becomes critical for optimizing return on investment. Current approaches predominantly rely on either optimization-based strategies or reinforcement learning techniques. However, optimization-based methods lack flexibility in adapting to dynamic market conditions, while reinforcement learning approaches often struggle to capture essential historical dependencies and observational patterns within the constraints of Markov Decision Process frameworks. To address these limitations, we propose AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control. The framework employs a high-level generative planner based on diffusion models to dynamically all...