《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》閱讀筆記
DeepFM: A Factorization-Machine based Neural Network for CTR Prediction 現有的針對CTR預估問題的解決方案，普遍對不同階的交叉特徵有着偏好，或者需要專家級特徵工程。文章提出了DeepFM模型，能夠實現端到端訓練，不需要額外的特徵工程，並且可以自動地提取交叉特徵。 CTR預估中，一個很重要的內容就是去挖掘點擊背後隱藏的特
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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. 04-《DeepFM: A Factorization-Machine based Neural Network for CTR Prediction》
- 4. A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction閱讀筆記
- 5. 【閱讀筆記】《ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation》
- 6. 《A self-attention based message passing neural network for predicting molecular lipophilicity》閱讀筆記
- 7. 【閱讀筆記】《ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks》
- 8. 論文Product-based Neural Network for User Response Prediction
- 9. [IJCAI 2017]A Dual-Stage Attention-Based Recurrent Neural Network for Time Series Prediction
- 10. 【閱讀筆記】《ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation》