Multi-Task Combinatorial Bandits for Budget Allocation
Lin Ge, Yang Xu, Jianing Chu, David Cramer, Fuhong Li, Kelly Paulson,, Rui Song

TL;DR
This paper introduces a multi-task combinatorial bandit framework for optimizing budget allocation across multiple advertising campaigns, leveraging Bayesian models and Thompson sampling to enhance decision-making under uncertainty.
Contribution
It presents a novel online system that combines hierarchical Bayesian modeling with flexible techniques and Thompson sampling for efficient budget allocation in advertising.
Findings
System effectively maximizes cumulative returns in experiments.
Demonstrates robustness and adaptability across different environments.
Integrates diverse modeling techniques for complex scenarios.
Abstract
Today's top advertisers typically manage hundreds of campaigns simultaneously and consistently launch new ones throughout the year. A crucial challenge for marketing managers is determining the optimal allocation of limited budgets across various ad lines in each campaign to maximize cumulative returns, especially given the huge uncertainty in return outcomes. In this paper, we propose to formulate budget allocation as a multi-task combinatorial bandit problem and introduce a novel online budget allocation system. The proposed system: i) integrates a Bayesian hierarchical model to intelligently utilize the metadata of campaigns and ad lines and budget size, ensuring efficient information sharing; ii) provides the flexibility to incorporate diverse modeling techniques such as Linear Regression, Gaussian Processes, and Neural Networks, catering to diverse environmental complexities; and…
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Taxonomy
TopicsAdvanced Bandit Algorithms Research · Auction Theory and Applications · Spreadsheets and End-User Computing
MethodsLinear Regression
