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arXiv Open Access 2024

Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System

Yang Zhao Chang Zhou Jin Cao Yi Zhao Shaobo Liu +2 lainnya
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Abstrak

This paper explores multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). We address this by treating scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. We introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and allows for strategy communication to boost overall performance. Our results show marked improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, confirming our method's efficacy in practical settings.

Topik & Kata Kunci

cs.LG cs.AI

Penulis (7)

Y

Yang Zhao

C

Chang Zhou

J

Jin Cao

Y

Yi Zhao

S

Shaobo Liu

C

Chiyu Cheng

X

Xingchen Li

Format Sitasi

APA MLA BibTeX
Zhao, Y., Zhou, C., Cao, J., Zhao, Y., Liu, S., Cheng, C. et al. (2024). Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System. https://arxiv.org/abs/2407.02759

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Informasi Jurnal
Tahun Terbit
2024
Bahasa
en
Sumber Database
arXiv
Akses
Open Access ✓