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Slate Recommendation Model.pdf

2208.06263.pdf
Preview of Slate Recommendation Model
🔗 Source: arxiv.org
📊 Size: 2.51 MB
📄 Pages: 11 pages
⬇️ Downloads: 129

Summary

Probabilistic Rank and Reward (PRR) is a scalable probabilistic model for personalized slate recommendation, allowing off-policy estimation of reward in scenarios where users interact with at most one item from a slate of K items. PRR combines reward and rank signals to learn the probability of a slate being successful, outperforming existing off-policy reward optimizing methods and being more scalable to large action spaces. It also allows fast delivery of recommendations using maximum inner product search (MIPS), making it suitable for low-latency domains like computational advertising.

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Description

Probabilistic Rank and Reward (PRR) is a scalable probabilistic model for personalized slate recommendation, allowing off-policy estimation of reward in...

Technical Information

  • File Format: PDF
  • File Size: 2.51 MB
  • Pages: 11
  • Language: EN
  • Total Downloads: 129
  • Last Updated: 3 hours ago

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This PDF document about Slate Recommendation Model provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Slate Recommendation Model.

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