论文Disguise Adversarial Networks for Click-through Rate Prediction
本文引入了一个对抗性学习框架来提高广告推荐CTR预测。使用DAN方法给广告化妆。DAN将一个不受欢迎的广告impression（non-click）伪装成一个有趣的，然后鼓励分类器将这些伪装的广告分类到正类的推荐中。在另一方面，鉴别器将通过无监督的信息理论分配策略对这些伪装的广告进行优化分配到其本身的类别。 DAN由GAN而来。GAN有许多不同的创新，然而它的中心概念仍然是生成器G和分类器D的一个
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相关文章
- 1. 论文笔记《Adversarial Networks for Prostate Cancer Detection》
- 2. 论文《Adversarial Reading Networks For Machine Comprehension》
- 3. 「论文翻译」Performance Study of Graph Convolutional Networks for Medical Prediction-Based Networks
- 4. 论文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. 论文译文《ANOMALY DETECTION WITH GENERATIVE ADVERSARIAL NETWORKS》
- 9. Deep Interest Network for Click-Through Rate Prediction
- 10. Binary Generative Adversarial Networks for Image Retrieval