Computer Science > Machine Learning
arXiv:2309.02669 (cs)
[Submitted on 6 Sep 2023]
Title:Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning
Authors:Tianchi Cai,Jiyan Jiang,Wenpeng Zhang,Shiji Zhou,Xierui Song,Li Yu,Lihong Gu,Xiaodong Zeng,Jinjie Gu,Guannan Zhang
View a PDF of the paper titled Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning, by Tianchi Cai and 9 other authors
View PDFAbstract:We study the budget allocation problem in online marketing campaigns that utilize previously collected offline data. We first discuss the long-term effect of optimizing marketing budget allocation decisions in the offline setting. To overcome the challenge, we propose a novel game-theoretic offline value-based reinforcement learning method using mixed policies. The proposed method reduces the need to store infinitely many policies in previous methods to only constantly many policies, which achieves nearly optimal policy efficiency, making it practical and favorable for industrial usage. We further show that this method is guaranteed to converge to the optimal policy, which cannot be achieved by previous value-based reinforcement learning methods for marketing budget allocation. Our experiments on a large-scale marketing campaign with tens-of-millions users and more than one billion budget verify the theoretical results and show that the proposed method outperforms various baseline methods. The proposed method has been successfully deployed to serve all the traffic of this marketing campaign.
| Comments: | WSDM 23, Best Paper Candidate |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2309.02669 [cs.LG] |
| (orarXiv:2309.02669v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2309.02669 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Marketing Budget Allocation with Offline Constrained Deep Reinforcement Learning, by Tianchi Cai and 9 other authors
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