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Reinforcement Learning For Explainable Recommendation.pdf

1906.05237.pdf
Preview of Reinforcement Learning for Explainable Recommendation
🔗 Source: arxiv.org
📊 Size: 1.38 MB
👤 Author: Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang
⬇️ Downloads: 1,106

Summary

Enhance recommendation systems using knowledge graphs (KG) for better performance and explainability.
- Solution: Propose Policy-Guided Path Reasoning (PGPR), a reinforcement learning (RL) approach for explainable recommendation.
- Contributions:
1. Formally define and interpret the reasoning process when incorporating KGs into recommendation.
2. Develop an RL approach with a soft reward strategy, user-conditional action pruning, and a multi-hop scoring function.
3. Design a policy-guided graph search algorithm to efficiently sample reasoning paths.
4. Extensive evaluation on large-scale real-world datasets, outperforming state-of-the-art methods.
- Key Features: Couples recommendation and interpretability by providing actual paths in a knowledge graph, enabling causal inference for decision-making.

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Description

A method for explainable recommendations using knowledge graph reasoning, providing interpretable paths.

Technical Information

  • File Format: PDF
  • File Size: 1.38 MB
  • Pages: 10
  • Language: EN
  • Author: Yikun Xian, Zuohui Fu, S. Muthukrishnan, Gerard de Melo, and Yongfeng Zhang
  • Total Downloads: 1,106
  • Last Updated: 1 week ago

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