- [ECIR 2016 RTB tutorials] (http://tutorial.computational-advertising.org/final-slides.pdf) by Weinan Zhang, Shuai Yuan, Jun Wang
- [Stanford Computational Advertising Course] (https://web.stanford.edu/class/msande239/) 2011
- [CVR Post-Click Conversion Modeling and Analysis for Non-Guaranteed Delivery Display Advertising] (http://people.csail.mit.edu/romer/papers/NGDAdvertisingWSDM12.pdf) Yahoo Labs, WSDM12 Keywords: Automated feature analysis (compound features), model update frequency. Model: Logistic Regression
- [CVR Finding the Right Consumer: Optimizing for Conversion in Display Advertising Campaigns] (https://www.researchgate.net/profile/Yandong_Liu/publication/221520060_Finding_the_right_consumer_optimizing_for_conversion_in_display_advertising_campaigns/links/02e7e51ae50d869ce1000000.pdf)Yahoo Labs, WSDM12 Keywords: Campaign metadata, learning across campaigns. Model: Logistic Regression, Linear SVM, Naive Bayes
- [CVR Estimating Conversion Rate in Display Advertising from Past Performance Data ] (https://pdfs.semanticscholar.org/379a/1c6d825f957f030cda8babc519738c224ca3.pdf) Turn, KDD12. Keywords: Data hierarchies Model: Logistic Regression
- [CTR Practical Lessons from Predicting Clicks on Ads at Facebook] (https://pdfs.semanticscholar.org/daf9/ed5dc6c6bad5367d7fd8561527da30e9b8dd.pdf) Facebook, ADKDD14, **Keywords:**Online training, Downsampling training, Model freshness and calibration Model: GBRT combined with Logistic Regression
- [CTR Ad Click Prediction: a View from the Trenches] (https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf) Google, KDD13, Keywords: Online learning
- [CTR Click-through Prediction for Advertising in Twitter Timeline] (http://www-personal.umich.edu/~lichengz/papers/kdd2015-li.pdf) Twitter, KDD15 Keywords: Online learning
- [CTR CTR Prediction for Contextual Advertising: Learning-to-Rank Approach］ (http://dl.acm.org/citation.cfm?id=2501978) Yahoo, ADKDD13 **Keywords:**use only clicks for training Models: SVM, LR
- [CTR A Two-Stage Ensemble of Diverse Models for Advertisement Ranking in KDD Cup 2012] (http://www.csie.ntu.edu.tw/~htlin/paper/doc/wskdd12cup.pdf) KDD cup 2012. Keywords: Ensemble models Models: LR, Naive Bayes, Ridge Regression, SVR, RLR, RankNet, CRR, Regression Based MF, Ranking Based MF.
- User Response Learning for Directly Optimizing Campaign Performance in Display Advertising CIKM, 2016
- A convolutional click prediction model CIKM, 2015
- Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks AAAI, 2014
- [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising] (https://arxiv.org/pdf/1305.3011.pdf) Turn, ACM, 2013
- [Programmatic buying bidding strategies with win rate and winning price estimation in real time mobile advertising] (http://link.springer.com/chapter/10.1007%2F978-3-319-06608-0_37) PAKDD, 2014
- Predicting Winning Price in Real Time Bidding with Censored Data, ACM, 2015
- Functional Bid Landscape Forecasting for Display Advertising, ECML/PKDD 2016
- Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising KDD, 2016
- [Bid Optimizing and Inventory Scoring in Targeted Online Advertising] (http://www0.cs.ucl.ac.uk/staff/w.zhang/rtb-papers/lin-bid.pdf), KDD 2012
- [Statistical Arbitrage Mining for Display Advertising] (http://arxiv.org/abs/1506.03837) KDD, 2015
- Budget Optimization for Sponsored Search: Censored Learning in MDPs UAI, 2012
- [Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation] (http://www.msr-waypoint.net/en-us/um/people/nikdev/pubs/rtb-perf.pdf) KDD, 2011
- Lift-Based Bidding in Ad Selection AAAI, 2016
- Optimal Real-Time Bidding for Display Advertising KDD, 2014
- Bid Landscape Forecasting in Online Ad Exchange Marketplace KDD. 2011
- [Understanding Behaviors that lead to Purchasing: A case Study of Pinterest] (https://cs.stanford.edu/people/jure/pubs/pinterest-kdd16.pdf) KDD, 2016
- [SEISMIC: A Self-Exciting Point Process Model for Predicting Tweet Popularity] (http://snap.stanford.edu/seismic/seismic.pdf) KDD, 2016