論文Disguise Adversarial Networks for Click-through Rate Prediction
本文引入了一個對抗性學習框架來提高廣告推薦CTR預測。使用DAN方法給廣告化妝。DAN將一個不受歡迎的廣告impression（non-click）僞裝成一個有趣的，然後鼓勵分類器將這些僞裝的廣告分類到正類的推薦中。在另一方面，鑑別器將通過無監督的信息理論分配策略對這些僞裝的廣告進行優化分配到其本身的類別。 DAN由GAN而來。GAN有許多不同的創新，然而它的中心概念仍然是生成器G和分類器D的一個
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相關文章
- 1. 論文筆記《Adversarial Networks for Prostate Cancer Detection》
- 2. 論文《Adversarial Reading Networks For Machine Comprehension》
- 3. 「論文翻譯」Performance Study of Graph Convolutional Networks for Medical Prediction-Based Networks
- 4. 論文Deep Character-Level Click-Through Rate Prediction for Sponsored Search
- 5. Echo-State Networks for Network Traffific Prediction
- 6. #Paper Reading# Deep Interest Network for Click-Through Rate Prediction
- 7. GD-GAN: Generative Adversarial Networks for Trajectory Prediction and Group Detection in Crowds
- 8. 論文譯文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 9. Deep Interest Network for Click-Through Rate Prediction
- 10. Binary Generative Adversarial Networks for Image Retrieval