Generative Adversarial User Model for Reinforcement Learning Based Recommendation System
Generative Adversarial User Model for Reinforcement Learning Based Recommendation System ICML 2019 摘要： 本文是推薦系統和強化學習結合起來的應用： 主要是2點： 1 我們開發了一個生成式的對抗學習(GAN)公式來模擬用戶的行爲動態，並恢復她的獎勵功能。通過聯合最小二乘優化算法，同時對這兩個分量進
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相關文章
- 1. Deep Learning Recommendation Model for Personalization and Recommendation Systems
- 2. AT-GAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
- 3. 論文筆記 Benchmarking Model-Based Reinforcement Learning
- 4. Model-Based Reinforcement Learning: Theory and Practice 譯文
- 5. MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection 走讀
- 6. [Reinforcement Learning] Model-Free Prediction
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- 8. [Reinforcement Learning] Model-Free Control
- 9. Reinforcement Learning（三）：Policy-Based
- 10. Reinforcement Learning強化學習系列之一：model-based learning