Summary of Cooperative Multi-agent Deep Reinforcement Learning in Content Ranking Optimization, by Zhou Qin et al.
Cooperative Multi-Agent Deep Reinforcement Learning in Content Ranking Optimization
by Zhou Qin, Kai Yuan, Pratik Lahiri, Wenyang Liu
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper proposes a reinforcement learning-based method for whole-page content ranking optimization, shifting from position-level optimization to whole-page level optimization. The authors formulate page-level CRO as a cooperative Multi-Agent Markov Decision Process and address it with a novel Multi-Agent Deep Deterministic Policy Gradient (MADDPG) model. MADDPG uses a “centralized training and decentralized execution” approach, supporting flexible and scalable joint optimization. Experiments demonstrate that MADDPG outperforms the deep bandits modeling by 25.7% on an offline CRO dataset from a leading e-commerce company. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make online shopping better by finding the best order for search results. Right now, different models decide what content to show at each position on the page. But this doesn’t always work well together, and can even hurt revenue at lower positions. The authors created a new way to optimize all positions on the page at once using reinforcement learning. They called it Multi-Agent Deep Deterministic Policy Gradient (MADDPG). This approach worked really well in tests, beating other methods by 25.7%. The authors think this could be useful for other problems like this. |
Keywords
» Artificial intelligence » Optimization » Reinforcement learning
