Wide & Deep Learning for Recommender Systems論文筆記
Wide & Deep Learning for Recommender Systems論文筆記 摘要 具有非線性特徵變換的廣義線性模型廣泛應用於具有稀疏輸入的大規模迴歸和分類問題。通過大量的跨產品特徵轉換來記憶特徵交互是有效的和可解釋的，而泛化則需要更多的特徵工程工作。在特徵工程較少的情況下，通過對稀疏特徵的低維密集嵌入學習，深層神經網絡可以更好地推廣到不可見的特徵組合。然而，當用戶-項目交互稀
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相關文章
- 1. 論文筆記 - Wide & Deep Learning for Recommender Systems
- 2. 論文筆記：Wide & Deep Learning for Recommender Systems
- 3. 《Wide & Deep Learning for Recommender Systems》論文總結
- 4. 論文閱讀：《Wide & Deep Learning for Recommender Systems》
- 5. Wide & Deep Learning for Recommender Systems
- 6. Wide &Deep Learning for Recommender Systems
- 7. #Paper Reading# Wide & Deep Learning for Recommender Systems
- 8. Wide & Deep Learning for Recommender Systems 翻譯
- 9. Wide & Deep Learning for Recommender Systems 模型實踐
- 10. 《Wide and deep learning in Recommender Systems》論文閱讀筆記