Summary of Multi-scenario Combination Based on Multi-agent Reinforcement Learning to Optimize the Advertising Recommendation System, by Yang Zhao et al.
Multi-Scenario Combination Based on Multi-Agent Reinforcement Learning to Optimize the Advertising Recommendation System
by Yang Zhao, Chang Zhou, Jin Cao, Yi Zhao, Shaobo Liu, Chiyu Cheng, Xingchen Li
First submitted to arxiv on: 3 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach to multi-scenario optimization on large platforms using multi-agent reinforcement learning (MARL). The authors frame scenarios like search, recommendation, and advertising as a cooperative, partially observable multi-agent decision problem. They introduce the Multi-Agent Recurrent Deterministic Policy Gradient (MARDPG) algorithm, which aligns different scenarios under a shared objective and enables strategy communication to boost overall performance. The results demonstrate significant improvements in metrics such as click-through rate (CTR), conversion rate, and total sales, validating the method’s effectiveness in practical settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses artificial intelligence to help big platforms make better decisions. Imagine you’re trying to get the best deal on a product online or seeing personalized ads. The authors use a type of AI called multi-agent reinforcement learning to help different scenarios work together and become better at making decisions. They created an algorithm that lets these scenarios share information and work together, which makes everything perform better. This helps platforms like search engines or social media get more clicks, conversions, and sales. |
Keywords
* Artificial intelligence * Optimization * Reinforcement learning
