Deep Neural Networks for YouTube Recommendations
問題 YouTube推薦在以下三個方面有極大的挑戰性： 規模大 新的item多 噪聲 由於稀疏性和一些無法觀測的外部因素，YouTube的歷史用戶行爲是十分難以預測的，而與內容相關的元數據也不能定義一個規整的結構。我們的算法需要足夠健壯以應對我們訓練數據的這些特性。 系統架構 系統中有兩個網絡，分別用於召回（candidate generation）和排序。 召回網絡從用戶行爲歷史中拿到event
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相關文章
- 1. Deep Neural Networks for Object Detection
- 2. Aggregated Residual Transformations for Deep Neural Networks
- 3. Neural Networks and Deep Learning -- Class 4: Deep Neural Networks
- 4. Improving Deep Neural Networks
- 5. Deep Learning 論文解讀——Session-based Recommendations with Recurrent Neural Networks
- 6. Neural Networks and Deep Learning -- Class 3: Shallow neural networks
- 7. Improving Deep Neural Networks [1]
- 8. Neural Networks and Deep Learning
- 9. Deep Reinforcement Learning for List-wise Recommendations
- 10. Improving Deep Neural Networks[3]