論文解析：Deep Reinforcement Learning for List-wise Recommendations
論文解析：Deep Reinforcement Learning for List-wise Recommendations 簡介 京東在強化學習推薦系統方面的工做，發表在KDD 2019web 背景 推薦系統存在的問題：算法 沒法經過與用戶的交互建模用戶的動態興趣變化 最大化單次ranking的最大收益，未必是長期的全局最大收益 ranking過程忽略了item之間的關聯 方法 強化學習 強化學
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相關文章
- 1. Deep Reinforcement Learning for List-wise Recommendations
- 2. 論文解析：Deep Reinforcement Learning for Page-wise Recommendations
- 3. 論文閱讀：(LIRD)Deep Reinforcement Learning for List-wise Recommendations
- 4. 【論文筆記】Deep Reinforcement Learning for Page-wise Recommendations
- 5. 論文解析——Deep Reinforcement Learning of Region Proposal Networks for Object Detection
- 6. Deep Reinforcement Learning for Dialogue Generation
- 7. Deep Reinforcement Learning for Join Order Enumeration
- 8. Deep Residual Learning 論文解析
- 9. 論文筆記：《Playing Atari with Deep Reinforcement Learning》
- 10. 【論文翻譯】Playing Atari with Deep Reinforcement Learning