04-《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》
1.背景 DeepFM，2017年被提出，是一個以並行形式結合了DNN和FM的模型。它被提出用於解決構建複雜特徵組合的問題。 在CTR預測中，學習用戶點擊行爲背後的隱藏特徵組合是非常重要的。這些隱藏特徵組合通常比較複雜，包括了低階和高階。我們需要注意是的，低階和高階的特徵組合是同樣重要的，我們不應該忽略任意一個。 構建特徵組合的問題，其實一直是CTR預測中的一個難題。 對於複雜高階特徵組合而言，多
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相關文章
- 1. DeepFM: A Factorization-Machine based Neural Network for CTR Prediction (2017)論文要點
- 2. #Paper Reading# DeepFM: a factorization-machine based neural network for CTR prediction
- 3. 《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》閱讀筆記
- 4. 論文Product-based Neural Network for User Response Prediction
- 5. [IJCAI 2017]A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
- 6. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction閱讀筆記
- 7. [ACL2015]A Dependency-Based Neural Network for Relation Classification
- 8. [ACL2015] A Dependency-Based Neural Network for Relation Classification
- 9. CTR預估模型之DeepFM
- 10. A perceptual quantization strategy for HEVC based on a convolutional neural network trained on natur