Combining Feature Importance and Bilinear feature Interaction for CTR Prediction (FiBiNET)
時間：2019.05 作者：Tongwen Huang, Zhiqi Zhang, Junlin Zhang Abstract 文中使用Squeeze-Excitation Network（SENET）動態學習特徵的重要性，並且使用bilinear function學習特徵組合 Model Structure SENET Layer Squeeze：用max/averag
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相關文章
- 1. Model-Agnostic Methods - Feature Interaction&Feature Importance
- 2. RecSys2019|優於DeepFM和XDeepFM的CTR模型FiBiNET
- 3. CTR學習筆記&代碼實現6-深度ctr模型 後浪 xDeepFM/FiBiNET
- 4. R programming for feature selection and regression
- 5. CTR預估 論文精讀(十)--xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
- 6. 淺談 CTR 預估模型發展史
- 7. 論文淺嘗 | Interaction Embeddings for Prediction and Explanation
- 8. categorical and ordinal feature
- 9. Reinforced Feature Points: Optimizing Feature Detection and Description for a High-Level(2020)(八)
- 10. 學習筆記：LAB Feature with Feature-centric Cascade for Fast and Accurate Face Detection