廣告ctr預估有什麼值得推薦的論文？
01-15
UCL的張偉楠在Github上整理過計算廣告領域的一份Paper List，非常有實用價值，強烈推薦關注。
GitHub - wnzhang/rtb-papers: A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
其中不僅涵蓋了CTR、CVR預估，還包括RTB競價策略，預算分配與控制，作弊監測，DMP、SSP、DSP系統設計等諸多非常有價值的領域，基本涵蓋了計算廣告的各個環節，不僅可以帶來很多科研上的idea，而且大部分易於工程實現。計算廣告從業者就別自己悶頭造輪子了，直接拿來快速提高廣告效果和系統性能吧！
結合本題，引用一下該List中CTR相關的paper如下：CTR/CVR Estimation
- Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization by Lili Shan, Lei Lin, Chengjie Sun, Xiaolong Wang. Electronic Commerce Research and Applications 2016
- Cost-sensitive Learning for Bidding in Online Advertising Auctions by Flavian Vasile, Damien Lefortier. Workshop on E-Commerce, NIPS 2015.
- A Convolutional Click Prediction Model by Qiang Liu, Feng Yu, Shu Wu, Liang Wang. CIKM 2015
- Factorization Machines with Follow-The-Regularized-Leader for CTR prediction in Display Advertising by Anh-Phuong Ta. BigData 2015.
- Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction by Weinan Zhang, Tianming Du, Jun Wang. ECIR 2016.
- Offline Evaluation of Response Prediction in Online Advertising Auctions by Olivier Chapelle. WWW 2015.
- Predicting Response in Mobile Advertising with Hierarchical Importance-Aware Factorization Machine by Richard J. Oentaryo et al. WSDM 2014.
- Estimating Conversion Rate in Display Advertising from Past Performance Data by Kuang-chih Lee et al. KDD 2012.
- Scalable Hands-Free Transfer Learning for Online Advertising by Brian Dalessandro et al. KDD 2014.
- Evaluating and Optimizing Online Advertising: Forget the click, but there are good proxies by Brian Dalessandro et al. SSRN 2012.
- Modeling Delayed Feedback in Display Advertising by Olivier Chapelle. KDD 2014.
- Ad Click Prediction: a View from the Trenches by H. Brendan McMahan. KDD 2013.
- Practical Lessons from Predicting Clicks on Ads at Facebook by Xinran He et al. ADKDD 2014.
wzhe06/Ad-papers: Papers on Computational Advertising
- Google：Ad Click Prediction：a View from the Trenches. pCTR使用LR, 通過Follow-The-Regularized-Leader (FTRL) Proximal演算法實現在線模型更新, 頻率學派. 寫的很好很細, 也有很多工程細節.
- Bing：Web-Scale Bayesian Click-Through Rate Prediction for Sponsored Search Advertising in Microsoft』s Bing Search Engine. Online Bayesian Probit Regression, 貝葉斯學派. 涉及到採樣演算法的模型一般訓練起來都比較費勁.
- Facebook：Practical Lessons from Predicting Clicks on Ads at Facebook. DT+LR. 和GBDT非常類似, 不同處在於用LR重新訓練了每棵樹投票的權重. 最近很火的xgboost, 在這一塊也是做了優化，利用二階導數信息得到更快收斂的步長. 樹模型處理不了高維特徵, 但與LR相比在處理連續特徵方面更有優勢.
- Ali：Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising. LR + Conjunction Feature + GroupLasso. Conjunction Feature鮮有耳聞, 要麼效果不好要麼計算量大. GroupLasso是sparse regularization的花活, 沒太大創新.
- 新趨勢是FM, 對於組合特徵有更好的泛化性.
這個就比較多了，隨便列幾個：Ensemble of Collaborative Filtering and Feature Engineered Models for Click Through Rate Prediction，Predicting Clicks:Estimating the Click-Through Rate for New Ads，Practical Lessons from Predicting Clicks on Ads at Facebook，Ad Click Prediction: a View from the Trenches，Click-Through Prediction for Sponsored Search Advertising with Hybrid Models等等，Google搜一下應該就能下了，還有kaggle上面也有相關的競賽及獲獎前幾名的論文
Olivier Chapelle （Principal research scientist in machine learning at Criteo）有一些。 http://olivier.chapelle.cc/pub.html
Chenyan Xiong, Taifeng Wang, Wenkui Ding, Yidong Shen and Tie-Yan Liu. Relational Click Prediction for Sponsored Search. WSDM 2012.
整理了一些計算廣告方面的乾貨，包括paper、dataset、slide、code、video，歡迎一起學習交流！計算廣告乾貨整理 - 雪倫的專欄 - 博客頻道 - CSDN.NET
花名，北冥乘海生，在計算廣告領域有不少的總結，還不錯～top的論文雅虎，谷歌的ctr團隊都貢獻不少
經典的BPR，你值得擁有
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