Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising

By Lin Yan, Wu-Jun Li, Gui-Rong Xue, Dingyi Han. 2014

138 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Han Zhu
2. Xiao Ma
3. Xiaoqiang Zhu
4. Guorui Zhou
5. Qingquan Song
6. Kun Gai
7. Xia Hu
8. Han Li

Citations in the corpus

• Deep Interest Network for Click-Through Rate Prediction (2017, methodology): “= ˝ n i=1 #impressioni ×AUCi ˝ n i=1 #impressioni , (10) where n is the number of users, #impressioni and AUCi are the number of impressions and AUC corresponding to the i-th user. Besides, we follow [25] to introduce RelaImpr metric to measure relative improvement over models. For a random guesser,”
• Towards Automated Neural Interaction Discovery for Click-Through Rate Prediction (2020, background): “edict the probability of a user clicking a target item. Since its inception, the mainstream models have roughly experienced three-stage evolution starting from linear regression and tree-based models [11, 20, 32], to interaction-based models [14, 26], and then the deep neural networks (DNNs) [7, 9, ”