Adaptive Budget Optimization for Multichannel Advertising Using Combinatorial Bandits

TL;DR

This paper introduces a simulation environment and a novel combinatorial bandit algorithm for adaptive budget allocation in multichannel digital advertising, effectively handling non-stationary market dynamics.

Contribution

It develops a realistic simulation platform and proposes an adaptive combinatorial bandit strategy with change-point detection for improved budget optimization.

Findings

Outperforms baseline strategies in real-world campaigns

Achieves higher rewards and lower regret

Effectively adapts to market changes

Abstract

Effective budget allocation is crucial for optimizing the performance of digital advertising campaigns. However, the development of practical budget allocation algorithms remain limited, primarily due to the lack of public datasets and comprehensive simulation environments capable of verifying the intricacies of real-world advertising. While multi-armed bandit (MAB) algorithms have been extensively studied, their efficacy diminishes in non-stationary environments where quick adaptation to changing market dynamics is essential. In this paper, we advance the field of budget allocation in digital advertising by introducing three key contributions. First, we develop a simulation environment designed to mimic multichannel advertising campaigns over extended time horizons, incorporating logged real-world data. Second, we propose an enhanced combinatorial bandit budget allocation strategy that leverages a saturating mean function and a targeted exploration mechanism with change-point detection. This approach dynamically adapts to changing market conditions, improving allocation efficiency by filtering target regions based on domain knowledge. Finally, we present both theoretical analysis and empirical results, demonstrating that our method consistently outperforms baseline strategies, achieving higher rewards and lower regret across multiple real-world campaigns.