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

Xinxin Yang, Yangyang Tang, Yikun Zhou, Yaolei Liu, Yun Li, Bo Yang

TL;DR

AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control, is proposed, which employs a high-level generative planner based on diffusion models to dynamically allocate budgets and constraints by effectively capturing historical context and temporal patterns.

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 allocate budgets and constraints by effectively capturing historical context and temporal patterns. We introduce a constraint enforcement mechanism to ensure compliance with specified constraints, along with a trajectory refinement mechanism that enhances adaptability to environmental changes through the utilization of historical data. The system further incorporates a control-based bidding algorithm that synergistically combines historical knowledge with real-time information, significantly improving both adaptability and operational efficacy. Extensive experiments conducted on large-scale offline datasets and through online A/B tests demonstrate the effectiveness of AHBid, yielding a 13.57% increase in overall return compared to existing baselines.

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

TL;DR

AHBid, an Adaptable Hierarchical Bidding framework that integrates generative planning with real-time control, is proposed, which employs a high-level generative planner based on diffusion models to dynamically allocate budgets and constraints by effectively capturing historical context and temporal patterns.

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 allocate budgets and constraints by effectively capturing historical context and temporal patterns. We introduce a constraint enforcement mechanism to ensure compliance with specified constraints, along with a trajectory refinement mechanism that enhances adaptability to environmental changes through the utilization of historical data. The system further incorporates a control-based bidding algorithm that synergistically combines historical knowledge with real-time information, significantly improving both adaptability and operational efficacy. Extensive experiments conducted on large-scale offline datasets and through online A/B tests demonstrate the effectiveness of AHBid, yielding a 13.57% increase in overall return compared to existing baselines.
Paper Structure (29 sections, 21 equations, 6 figures, 4 tables, 2 algorithms)

This paper contains 29 sections, 21 equations, 6 figures, 4 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Hierarchical Bidding Framework
  3. Problem Formulation
  4. Hierarchical Framework
  5. AHBid
  6. Framework Overview
  7. Learning the Planner
  8. Constraint Enforcement
  9. Learning the Bidder
  10. Training and Inference
  11. Experiments
  12. Experimental Setup
  13. Dataset.
  14. Offline Evaluation System.
  15. Evaluation Metrics.
  16. ...and 14 more sections

Figures (6)

  • Figure 1: Illustration of auto-bidding system applications in multi-channel scenarios.
  • Figure 2: Comparative analysis of advertising performance across channels.
  • Figure 3: The overall procedure of AHBid. During training, the planner incrementally adds noise to goal trajectories and learns denoising-based reconstruction. During inference, it iteratively denoises and refines a sampled trajectory using historical data from prior stages. The optimized trajectory subsequently guides the auto-bidder in adjusting parameters and determining real-time optimal bids.
  • Figure 4: Hyper-parameter tuning.
  • Figure 5: State transition in one episode.
  • ...and 1 more figures