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https://github.com/wnzhang/rtb-papers

A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.
https://github.com/wnzhang/rtb-papers

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A collection of research and survey papers of real-time bidding (RTB) based display advertising techniques.

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# Paper Collection of Real-Time Bidding



This is a collection of research and review papers of real-time bidding (RTB) based display advertising techniques. The sharing principle of these references here is for research. If any authors do not want their paper to be listed here, please feel free to contact [Weinan Zhang](http://wnzhang.net).



**You are more than welcome to update this list!** 


If you find a paper about RTB techniques or review which is not listed here, please

* fork this repository, add it and merge back;

* or email [Weinan Zhang](http://wnzhang.net) (wnzhang [AT] sjtu.edu.cn);

* or report an issue here.



## Books/Monographs

* [Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting](https://arxiv.org/abs/1610.03013) by Jun Wang, Weinan Zhang and Shuai Yuan. ArXiv 2016.



## Tutorials

* [Learning, Prediction and Optimisation in RTB Display Advertising](http://wnzhang.net/slides/cikm-16-rtb-full.pdf) by Weinan Zhang and Jian Xu. CIKM 2016.

* [Real-Time Bidding based Display Advertising: Mechanisms and Algorithms](http://wnzhang.net/slides/ecir16-rtb.pdf) by Jun Wang, Shuai Yuan and Weinan Zhang. ECIR 2016.

* [Real-Time Bidding: A New Frontier of Computational Advertising Research](https://dl.acm.org/citation.cfm?id=2697041) by Shuai Yuan and Jun Wang. WSDM 2015.

* [Research Frontier of Real-Time Bidding based Display Advertising](http://wnzhang.net/slides/rtb-frontier-2015.pdf) by Weinan Zhang. Beijing 2015.



## Review Papers

* [A Survey on Bid Optimization in Real-Time Bidding Display Advertising](https://apex.sjtu.edu.cn/public/files/members/20231211/2023-rtb-bidding-survey.pdf) by Weitong Ou, Bo Chen, Xinyi Dai, Weinan Zhang, Weiwen Liu, Ruiming Tang, Yong Yu. TKDD 2023.

* [A Survey on Real Time Bidding Advertising](http://wnzhang.net/share/rtb-papers/rtb-survey.pdf) by Yong Yuan. Service Operations and Logistics 2014.

* [Real-time Bidding for Online Advertising: Measurement and Analysis](http://wnzhang.net/share/rtb-papers/rtb-analysis.pdf) by Shuai Yuan, Jun Wang, Xiaoxue Zhao. ADKDD 2013.

* [Ad Exchanges: Research Issues](http://wnzhang.net/share/rtb-papers/adx.pdf) by S. Muthukrishnan. Internet and network economics 2009.



## Demand-Side Platform (DSP) Techniques



### CTR/CVR Estimation

* [A Nonparametric Delayed Feedback Model for Conversion Rate Prediction](https://arxiv.org/pdf/1802.00255.pdf) by Yuya Yoshikawa and Yusaku Imai. ArXiv 2018.

* [Robust Factorization Machines for User Response Prediction](http://wnzhang.net/share/rtb-papers/rfm-www.pdf) by Surabhi Punjabi and Priyanka Bhatt. WWW 2018.

* [Field-weighted Factorization Machines for Click-Through Rate Prediction in Display Advertising](http://wnzhang.net/share/rtb-papers/fwfm-www.pdf) by Junwei Pan et al. WWW 2018.

* [Deep & Cross Network for Ad Click Predictions](https://arxiv.org/pdf/1708.05123.pdf) by Ruoxi Wang et al. ADKDD & TargetAd 2017.

* [Ranking and Calibrating Click-Attributed Purchases in Performance Display Advertising](https://drive.google.com/file/d/0BwF-hgLDpCD6UTlYLXBZX1BwWHc/view) by Sougata Chaudhuri et al. ADKDD 2017.

* [A Practical Framework of Conversion Rate Prediction for Online Display Advertising](https://drive.google.com/file/d/0BwF-hgLDpCD6eENMRFo2dzF4NEk/view) by Quan Lu et al. ADKDD 2017.

* [An Ensemble-Based Approach to Click-Through Rate Prediction for Promoted Listings at Etsy](https://arxiv.org/pdf/1711.01377.pdf) by Kamelia Aryafar et al. ADKDD 2017.

* [Deep Interest Network for Click-Through Rate Prediction](https://arxiv.org/pdf/1706.06978.pdf) by Guorui Zhou et al. ArXiv 2017.

* [DeepFM: A Factorization-Machine based Neural Network for CTR Prediction](https://arxiv.org/pdf/1703.04247.pdf) by Huifeng Guo et al. IJCAI 2017

* [Learning Piece-wise Linear Models from Large Scale Data for Ad Click Prediction](https://arxiv.org/pdf/1704.05194.pdf)  by Kun Gai, Xiaoqiang Zhu, Han Li, et al. Arxiv 2017.

* [SEM: A Softmax-based Ensemble Model for CTR Estimation in Real-Time Bidding Advertising](http://wnzhang.net/share/rtb-papers/softmax-ensemble.pdf) by Wen-Yuan Zhu et al. BigComp 2017.

* [Neural Feature Embedding for User Response Prediction in Real-Time Bidding (RTB)](https://arxiv.org/pdf/1702.00855v1.pdf) by Enno Shioji, Masayuki Arai. ArXiv 2017.

* [Field-aware Factorization Machines in a Real-world Online Advertising System](https://arxiv.org/pdf/1701.04099.pdf) by Yuchin Juan, Damien Lefortier, Olivier Chapelle. ArXiv 2017.

* [Product-based Neural Networks for User Response Prediction](https://arxiv.org/pdf/1611.00144.pdf) by Yanru Qu et al. ICDM 2016.

* [Sparse Factorization Machines for Click-through Rate Prediction](http://staff.ustc.edu.cn/~cheneh/paper_pdf/2016/Zhen-Pan-ICDM.pdf) by Zhen Pan et al. ICDM 2016.

* [Deep CTR Prediction in Display Advertising](http://wnzhang.net/share/rtb-papers/deep-ctr-display.pdf) by Junxuan Chen et al. MM 2016.

* [Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising](http://wnzhang.net/papers/unbias-kdd.pdf) by Weinan Zhang, Tianxiong Zhou, Jun Wang, Jian Xu. KDD 2016.

* [Large Scale CVR Prediction through Dynamic Transfer Learning of Global and Local Features](http://proceedings.mlr.press/v53/yang16.pdf) by Hongxia Yang et al. BIGMINE 2016.

* [Predicting ad click-through rates via feature-based fully coupled interaction tensor factorization](http://wnzhang.net/share/rtb-papers/fctf.pdf) by Lili Shan, Lei Lin, Chengjie Sun, Xiaolong Wang. Electronic Commerce Research and Applications 2016.

* [User Response Learning for Directly Optimizing Campaign Performance in Display Advertising](http://apex.sjtu.edu.cn/public/files/members/20161024/opt-ctr-bid.pdf) by Kan Ren, Weinan Zhang, Yifei Rong, Haifeng Zhang, Yong Yu, Jun Wang. CIKM 2016.

* [Cost-sensitive Learning for Utility Optimization in Online Advertising Auctions](https://arxiv.org/pdf/1603.03713.pdf) by Flavian Vasile, Damien Lefortier, Olivier Chapelle. Extension under-review of the paper presented at the Workshop on E-Commerce, NIPS 2015.

* [A Convolutional Click Prediction Model](http://wnzhang.net/share/rtb-papers/cnn-ctr.pdf) by Qiang Liu, Feng Yu, Shu Wu, Liang Wang. CIKM 2015.

* [Factorization Machines with Follow-The-Regularized-Leader for CTR prediction in Display Advertising](http://wnzhang.net/share/rtb-papers/fm-ftrl.pdf) by Anh-Phuong Ta. BigData 2015.

* [Deep Learning over Multi-field Categorical Data: A Case Study on User Response Prediction](http://wnzhang.net/share/rtb-papers/deep-ctr.pdf) by Weinan Zhang, Tianming Du, Jun Wang. ECIR 2016.

* [Offline Evaluation of Response Prediction in Online Advertising Auctions](http://wnzhang.net/share/rtb-papers/ctr-bid.pdf) by Olivier Chapelle. WWW 2015.

* [Simple and Scalable Response Prediction for Display Advertising](http://wnzhang.net/share/rtb-papers/ctr-chapelle.pdf) by Olivier Chapelle	Criteo, Eren Manavoglu, Romer Rosales. ACM TIST 2014.

* [Predicting Response in Mobile Advertising with Hierarchical Importance-Aware Factorization Machine](http://wnzhang.net/share/rtb-papers/fm-ctr.pdf) by Richard J. Oentaryo et al. WSDM 2014.

* [Scalable Hierarchical Multitask Learning Algorithms for Conversion Optimization in Display Advertising](https://research.google/pubs/pub42498/) by Amr Ahmed et al. WSDM 2014.

* [Scalable Hands-Free Transfer Learning for Online Advertising](http://wnzhang.net/share/rtb-papers/transfer-ctr.pdf) by Brian Dalessandro et al. KDD 2014.

* [Modeling Delayed Feedback in Display Advertising](http://wnzhang.net/share/rtb-papers/delayed-feedback.pdf) by Olivier Chapelle. KDD 2014.

* [Practical Lessons from Predicting Clicks on Ads at Facebook](http://wnzhang.net/share/rtb-papers/fb-ad-ctr.pdf) by Xinran He et al. ADKDD 2014.

* [Ad Click Prediction: a View from the Trenches](https://www.eecs.tufts.edu/~dsculley/papers/ad-click-prediction.pdf) by H. Brendan McMahan. KDD 2013.

* [Estimating Conversion Rate in Display Advertising from Past Performance Data](http://wnzhang.net/share/rtb-papers/cvr-est.pdf) by Kuang-chih Lee et al. KDD 2012.

* [Evaluating and Optimizing Online Advertising: Forget the click, but there are good proxies](http://wnzhang.net/share/rtb-papers/forget-click.pdf) by Brian Dalessandro et al. SSRN 2012.



### Bid Landscape

* [Arbitrary Distribution Modeling with Censorship in Real-Time Bidding Advertising](https://doi.org/10.1145/3534678.3539048) by Xu Li, et al. KDD 2022.

* [Deep Landscape Forecasting for Real-time Bidding Advertising](https://arxiv.org/abs/1905.03028) by Kan Ren, et al. KDD 2019.

* [Deep Censored Learning of the Winning Price in the Real Time Bidding](https://dl.acm.org/citation.cfm?id=3219819.3220066) by Wush Wu, et al. KDD 2018.

* [Predicting Winning Price in Real Time Bidding with Censored Data](http://wnzhang.net/share/rtb-papers/win-price-pred.pdf) by Wush Chi-Hsuan Wu, Mi-Yen Yeh, Ming-Syan Chen. KDD 2015.

* [Handling Forecast Errors While Bidding for Display Advertising](http://wnzhang.net/share/rtb-papers/forecast-err.pdf) by Kevin J. Lang, Benjamin Moseley, Sergei Vassilvitskii. WWW 2012.

* [Bid Landscape Forecasting in Online Ad Exchange Marketplace](http://wnzhang.net/share/rtb-papers/bid-lands.pdf) by Ying Cui et al. KDD 2011.

* [Functional Bid Landscape Forecasting for Display Advertising](http://apex.sjtu.edu.cn/public/files/members/20160929/functional-bid-lands.pdf) by Yuchen Wang et al. ECML-PKDD 2016.



### Bidding Strategies

* [Risk-Aware Bid Optimization for Online Display Advertisement](https://dl.acm.org/doi/10.1145/3511808.3557436) by Rui Fan, Erick Delage. CIKM 2022

* [A Cooperative-Competitive Multi-Agent Framework for Auto-bidding in Online Advertising](https://dl.acm.org/doi/10.1145/3488560.3498373) by Chao Wen et al. WSDM 2022.

* [ROI-Constrained Bidding via Curriculum-Guided Bayesian Reinforcement Learning](https://doi.org/10.1145/3534678.3539211) by Haozhe Wang et al. KDD 2022.

* [An Efficient Deep Distribution Network for Bid Shading in First-Price Auctions](https://dl.acm.org/doi/10.1145/3447548.3467167) by Tian Zhou et al. KDD 2021.

* [MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm](https://dl.acm.org/doi/10.1145/3447548.3467113) by Wei Zhang et al. KDD 2021.

* [Adaptive Bid Shading Optimization of First-Price Ad Inventory](https://ieeexplore.ieee.org/document/9482665/) by Niklas Karlsson et al. ACC 2021.

* [A Unified Solution to Constrained Bidding in Online Display Advertising](https://dl.acm.org/doi/10.1145/3447548.3467199) by Yue He et al. KDD 2021.

* [Multi-Agent Cooperative Bidding Games for Multi-Objective Optimization in e-Commercial Sponsored Search](https://dl.acm.org/doi/10.1145/3447548.3467204) by Ziyu Guan et al.. KDD 2021.

* [Optimized Cost per Mille in Feeds Advertising](https://dl.acm.org/doi/pdf/10.5555/3398761.3398918) by Pingzhong Tang et al. AAMAS 2020.

* [Unbiased Lift-based Bidding System](http://arxiv.org/abs/2007.04002) by Daisuke Moriwaki et al. ArXiv 2020.

* [Budget-Constrained Real-Time Bidding Optimization: Multiple Predictors Make It Better](https://dl.acm.org/doi/10.1145/3375393) by Chi-Chun Lin et al. TKDD 2020.

* [Learning to Bid Optimally and Efficiently in Adversarial First-price Auctions](http://arxiv.org/abs/2007.04568) by Yanjun Han et al. Arxiv 2020.

* [Bid Shading in The Brave New World of First-Price Auctions](https://dl.acm.org/doi/10.1145/3340531.3412689) by Djordje Gligorijevic et al. CIKM 2020.

* [Scalable Multi-objective Optimization in Programmatic Advertising via Feedback Control](https://ieeexplore.ieee.org/document/9683668) by Niklas Karlsson. CDC 2021.

* [Reinforcement Learning with Sequential Information Clustering in Real-Time Bidding](https://dl.acm.org/doi/10.1145/3357384.3358027) by Junwei Lu et al. CIKM 2019.

* [AiAds: Automated and Intelligent Advertising System for Sponsored Search](https://dl.acm.org/doi/10.1145/3292500.3330782) by Xiao Yang et al. KDD 2019.

* [Feedback Control in Programmatic Advertising: The Frontier of Optimization in Real-Time Bidding](http://wnzhang.net/share/rtb-papers/fc-bidding.pdf) by Niklas Karlsson. IEEE Control Systems Magazine 2020.

* [Bid Optimization by Multivariable Control in Display Advertising](https://dl.acm.org/doi/pdf/10.1145/3292500.3330681) by Xun Yang et al. KDD 2019.

* [Recurrent Neural Networks for Stochastic Control in Real-Time Bidding](https://dl.acm.org/citation.cfm?id=3330749) by Nicolas Grislain et al. KDD 2019.

* [Optimal Bidding Strategy for Brand Advertising](https://www.ijcai.org/proceedings/2018/0059.pdf) by Takanori Maehara et al. IJCAI 2018.

* [Bidding Machine: Learning to Bid for Directly Optimizing Profits in Display Advertising](https://arxiv.org/abs/1803.02194) by Kan Ren et al. TKDE 2018.

* [Budget Constrained Bidding by Model-free Reinforcement Learning in Display Advertising](https://arxiv.org/pdf/1802.08365.pdf) by Di Wu et al. ArXiv 2018.

* [Real-Time Bidding with Multi-Agent Reinforcement Learning in Display Advertising](https://arxiv.org/pdf/1802.09756.pdf) by Junqi Jin et al. ArXiv 2018.

* [Deep Reinforcement Learning for Sponsored Search Real-time Bidding](https://arxiv.org/pdf/1803.00259.pdf) by Jun Zhao et al. ArXiv 2018.

* [LADDER: A Human-Level Bidding Agent for Large-Scale Real-Time Online Auctions](https://arxiv.org/pdf/1708.05565.pdf) by Yu Wang et al. ArXiv 2017.

* [Improving Real-Time Bidding Using a Constrained Markov Decision Process](http://wnzhang.net/share/rtb-papers/rtb-cmdp.pdf) by Manxing Du et al. ADMA 2017.

* [Attribution Modeling Increases Efficiency of Bidding in Display Advertising](https://arxiv.org/pdf/1707.06409.pdf) by Eustache Diemert et al. ADKDD 2017.

* [Profit Maximization for Online Advertising Demand-Side Platforms](https://arxiv.org/pdf/1706.01614.pdf) by Paul Grigas et al. ArXiv 2017.

* [Real-Time Bidding by Reinforcement Learning in Display Advertising](http://wnzhang.net/papers/rlb.pdf) by Han Cai et al. WSDM 2017.

* [Managing Risk of Bidding in Display Advertising](http://wnzhang.net/papers/risk-bid.pdf) by Haifeng Zhang et al. WSDM 2017.

* [Optimized Cost per Click in Taobao Display Advertising](https://arxiv.org/pdf/1703.02091.pdf) by Han Zhu et al. ArXiv 2017.

* [Combining Powers of Two Predictors in Optimizing Real-Time Bidding Strategy under Constrained Budget](http://wnzhang.net/share/rtb-papers/two-pred-bid.pdf) by Chi-Chun Lin et al. CIKM 2016.

* [Joint Optimization of Multiple Performance Metrics in Online Video Advertising](http://www.kdd.org/kdd2016/papers/files/adp0722-geyikA.pdf) by Sahin Cem Geyik et al. KDD 2016.

* [Optimal Real-Time Bidding for Display Advertising](http://discovery.ucl.ac.uk/1496878/1/weinan-zhang-phd-2016.pdf) by Weinan Zhang. PhD Thesis 2016.

* [Bid-aware Gradient Descent for Unbiased Learning with Censored Data in Display Advertising](http://wnzhang.net/papers/unbias-kdd.pdf) by Weinan Zhang, Tianxiong Zhou, Jun Wang, Jian Xu. KDD 2016.

* [Lift-Based Bidding in Ad Selection](http://wnzhang.net/share/rtb-papers/lift-bidding.pdf) by Jian Xu et al. AAAI 2016.

* [Feedback Control of Real-Time Display Advertising](http://wnzhang.net/papers/fc-wsdm.pdf) by Weinan Zhang et al. WSDM 2016.

* [Optimal Real-Time Bidding Strategies](http://arxiv.org/abs/1511.08409) by Joaquin Fernandez-Tapia, Olivier Guéant, Jean-Michel Lasry. ArXiv 2015.

* [Programmatic Buying Bidding Strategies with Win Rate and Winning Price Estimation in Real Time Mobile Advertising](http://wnzhang.net/share/rtb-papers/bid-drawbridge.pdf) by Xiang Li and Devin Guan. PAKDD 2014.

* [Statistical modeling of Vickrey auctions and applications to automated bidding strategies](https://www.researchgate.net/publication/283579660_Statistical_modeling_of_Vickrey_auctions_and_applications_to_automated_bidding_strategies) by Joaquin Fernandez-Tapia. Working paper.

* [Statistical Arbitrage Mining for Display Advertising](http://wnzhang.net/share/rtb-papers/rtb-arbitrage.pdf) by Weinan Zhang, Jun Wang. KDD 2015.

* [Real-Time Bidding rules of thumb: analytically optimizing the programmatic buying of ad-inventory](http://wnzhang.net/share/rtb-papers/opt-prog-buy.pdf) by Joaquin Fernandez-Tapia. SSRN 2015.

* [Optimal Real-Time Bidding for Display Advertising](http://wnzhang.net/share/rtb-papers/optimal-rtb.pdf) by Weinan Zhang, Shuai Yuan, Jun Wang. KDD 2014.

* [Bid Optimizing and Inventory Scoring in Targeted Online Advertising](http://wnzhang.net/share/rtb-papers/lin-bid.pdf) by Claudia Perlich et al. KDD 2012.

* [Real-Time Bidding Algorithms for Performance-Based Display Ad Allocation](http://wnzhang.net/share/rtb-papers/rtb-perf-bid.pdf) by Ye Chen et al. KDD 2011.



### Budget Pacing & Frequency/Recency Capping

* [An Effective Budget Management Framework for Real-Time Bidding in Online Advertising](https://ieeexplore.ieee.org/abstract/document/8976169) by Mengjuan Liu et al. IEEE Access 2020.

* [Impression Pacing for Jobs Marketplace at LinkedIn](https://dl.acm.org/doi/pdf/10.1145/3340531.3412711) by Sahin Cem Geyik et al. CIKM 2020.

* [Dynamic Bidding Strategies with Multivariate Feedback Control for Multiple Goals in Display Advertising](https://arxiv.org/ftp/arxiv/papers/2007/2007.00426.pdf) by Michael Tashman et al. ArXiv 2020.

* [Multiplicative Pacing Equilibria in Auction Markets](https://arxiv.org/abs/1706.07151) by Vincent Conitzer et al. ArXiv 2020.

* [Soft Frequency Capping for Improved Ad Click Prediction in Yahoo Gemini Native](https://dl.acm.org/doi/pdf/10.1145/3357384.3357801) by Michal Aharon et al. CIKM 2019.

* [Exploring Optimal Frequency Caps in Real Time Bidding Advertising](https://www.researchgate.net/publication/309588181_Exploring_Optimal_Frequency_Caps_in_Real_Time_Bidding_Advertising) by Rui Qin et al. SocialCom 2016.

* [Research on the Frequency Capping Issue in RTB Advertising：A Computational Experiment Approach](https://www.researchgate.net/publication/304294525_Research_on_the_frequency_capping_issue_in_RTB_advertising_A_computational_experiment_approach) by Rui Qin et al. CAC 2015.

* [From 0.5 Million to 2.5 Million: Efficiently Scaling up Real-Time Bidding](http://wnzhang.net/share/rtb-papers/turn-throatling.pdf) by Jianqian Shen et al. ICDM 2015.

* [Smart Pacing for Effective Online Ad Campaign Optimization](http://wnzhang.net/share/rtb-papers/throatling-pacing.pdf) by Jian Xu et al. KDD 2015.

* [An analytical solution to the budget-pacing problem in programmatic advertising](https://www.researchgate.net/publication/283579658_An_analytical_solution_to_the_budget-pacing_problem_in_programmatic_advertising) by Joaquin Fernandez-Tapia. Working paper.

* [Adaptive Targeting for Online Advertisement](http://wnzhang.net/share/rtb-papers/adaptive-targeting.pdf) by Andrey Pepelyshev, Yuri Staroselskiy, Anatoly Zhigljavsky. Machine Learning, Optimization, and Big Data 2015.

* [Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising](http://wnzhang.net/share/rtb-papers/budget-smooth.pdf) by Kuang-Chih Lee, Ali Jalali, Ali Dasdan. ADKDD 2013.

* [Budget Pacing for Targeted Online Advertisements at LinkedIn](http://wnzhang.net/share/rtb-papers/linkedin-pacing.pdf) by Deepak Agarwal et al. KDD 2014.

* [Frequency Capping in Online Advertising](http://theory.epfl.ch/moranfe/Publications/WADS2011.pdf) by Niv Buchbinder et al. WADS 2011.

* [Adaptive bidding for display advertising](http://www.arpitaghosh.com/papers/fp632-ghosh.pdf ) by Ghosh, A., Rubinstein, B. I, Vassilvitskii, S., and Zinkevich, M.  2009



### Fraud Detection

* [Independent Auditing of Online Display Advertising Campaigns](http://www.it.uc3m.es/~rcuevas/papers/p120-callejo.pdf) by 	Patricia Callejo et al. HotNets 2016.

* [Using Co-Visitation Networks For Classifying Non-Intentional Traffic](http://wnzhang.net/share/rtb-papers/co-visit-fraud.pdf) by Ori Stitelman et al. Dstillery 2013.

* [Impression Fraud in On-line Advertising via Pay-Per-View Networks](http://0b4af6cdc2f0c5998459-c0245c5c937c5dedcca3f1764ecc9b2f.r43.cf2.rackcdn.com/12305-sec13-paper_springborn.pdf) by Kevin Springborn, Paul Barford. USENIX Security Symposium 2013.

* [Understanding Fraudulent Activities in Online Ad Exchanges](http://conferences.sigcomm.org/imc/2011/docs/p279.pdf) by Brett Stone-Grosset et al. IMC 2011.



### Market Segmentation

* [Optimizing the Segmentation Granularity for RTB Advertising Markets with a Two-stage Resale Model](https://www.researchgate.net/publication/312173822_Optimizing_the_Segmentation_Granularity_for_RTB_Advertising_Markets_with_a_Two-stage_Resale_Model) By Rui Qin et al. SMC 2016.

* [Optimizing Market Segmentation Granularity in RTB Advertising: A Computational Experimental Study](https://www.researchgate.net/publication/309588977_Optimizing_Market_Segmentation_Granularity_in_RTB_Advertising_A_Computational_Experimental_Study) by Rui Qin et al. SocialCom 2016.

* [Analyzing the Segmentation Granularity of RTB Advertising Markets：A Computational Experiment Approach](https://www.researchgate.net/publication/300238564_Analyzing_the_Segmentation_Granularity_of_RTB_Advertising_Markets_A_Computational_Experiment_Approach) by Rui Qin et al. SMP 2015.



## Supply-Side Platform (SSP) Techniques

* [Feedback Control-Based Publisher Revenue Maximization in Online Advertising](https://ieeexplore.ieee.org/document/9786041) by Niklas Karlsson et al.  IEEE Control System Letters 2022.

* [Learning Algorithms for Second-Price Auctions with Reserve](http://jmlr.org/papers/volume17/14-499/14-499.pdf) by Mehryar Mohri and Andres Munoz Medina. JMLR 2016.

* [Optimal Reserve Prices in Upstream Auctions: Empirical Application on Online Video Advertising](http://www.kdd.org/kdd2016/papers/files/rpp1142-alcobendas-lisbonaA.pdf) by Miguel Angel Alcobendas, Sheide Chammas and Kuang-chih Lee. KDD 2016.

* [Optimal Allocation of Ad Inventory in Real-Time Bidding Advertising Markets](https://www.researchgate.net/publication/312173826_Optimal_Allocation_of_Ad_Inventory_in_Real-Time_Bidding_Advertising_Markets) by Juanjuan Li et al. SMC 2016.

* [A Dynamic Pricing Model for Unifying Programmatic Guarantee and Real-Time Bidding in Display Advertising](http://arxiv.org/pdf/1405.5189.pdf) by Bowei Chen, Shuai Yuan and Jun Wang. ADKDD 2014.

* [An Empirical Study of Reserve Price Optimisation in Real-Time Bidding](http://wnzhang.net/share/rtb-papers/reserve-price.pdf) by Shuai Yuan et al. KDD 2014.

* [Information Disclosure in Real-Time Bidding Advertising Markets](http://wnzhang.net/share/rtb-papers/rtb-info.pdf) by Juanjuan Li, Yong Yuan, Rui Qin. SOLI 2014.



## Data Management Platform (DMP) Techniques

* [A Sub-linear, Massive-scale Look-alike Audience Extension System](http://proceedings.mlr.press/v53/ma16.html)

  by Qiang Ma, Musen Wen, Zhen Xia, Datong Chen. KDD 2016 / PMLR 2016

* [Audience Expansion for Online Social Network Advertising](http://www.kdd.org/kdd2016/papers/files/adf0483-liuA.pdf) by Haishan Liu et al. KDD 2016.

* [Implicit Look-alike Modelling in Display Ads: Transfer Collaborative Filtering to CTR ](http://wnzhang.net/share/rtb-papers/cf-ctr.pdf) by Weinan Zhang, Lingxi Chen, Jun Wang. ECIR 2016.

* [Pleasing the advertising oracle: Probabilistic prediction from sampled, aggregated ground truth](http://wnzhang.net/share/rtb-papers/sample-pred.pdf) by Melinda Han Williams et al. ADKDD 2014.

* [Focused matrix factorization for audience selection in display advertising](http://static.googleusercontent.com/media/research.google.com/zh-CN//pubs/archive/40489.pdf) by Kanagal B et al. ICDE 2013.



## Conversion Attribution

* [Causal Models for Real Time Bidding with Repeated User Interactions](https://dl.acm.org/doi/10.1145/3447548.3467280) by Martin Bompaire. KDD 2021.

* [Learning Multi-touch Conversion Attribution with Dual-attention Mechanisms for Online Advertising](https://arxiv.org/abs/1808.03737) by Kan Ren, Yuchen Fang, Weinan Zhang, et al. CIKM 2018.

* [Additional Multi-Touch Attribution for Online Advertising](http://www.saying.ren/paper/ji-amta.pdf) by Wendi Ji, et al. AAAI 2017.

* [Multi-Touch Attribution in Online Advertising with Survival Theory](http://wnzhang.net/share/rtb-papers/attr-survival.pdf) by Ya Zhang, Yi Wei, and Jianbiao Ren. ICDM 2014.

* [Multi-Touch Attribution Based Budget Allocation in Online Advertising](http://wnzhang.net/share/rtb-papers/mta-budget-allocation.pdf) by Sahin Cem Geyik, Abhishek Saxena, Ali Dasdan. ADKDD 2014.

* [Causally Motivated Attribution for Online Advertising](http://wnzhang.net/share/rtb-papers/causual-conv-att.pdf). by Brian Dalessandro et al. ADKDD 2012.

* [Data-driven Multi-touch Attribution Models](http://wnzhang.net/share/rtb-papers/data-conv-att.pdf). by Xuhui Shao, Lexin Li. KDD 2011.



## Ad Exchanges, Mechanisms and Game Theory



* [Optimal Mechanisms for Value Maximizers with Budget Constraints via Target Clipping](https://doi.org/10.1145/3490486.3538333) by Santiago R. Balseiro et al. EC 2022.

* [Optimizing Multiple Performance Metrics with Deep GSP Auctions for E-commerce Advertising](http://arxiv.org/abs/2012.02930) by Zhilin Zhang et al. ArXiv 2021.

* [Boosted Second Price Auctions: Revenue Optimization for Heterogeneous Bidders](https://dl.acm.org/doi/10.1145/3447548.3467454) by Negin Golrezaei. KDD 2021.

* [Towards Efficient Auctions in an Auto-bidding World](https://dl.acm.org/doi/10.1145/3442381.3450052) by Yuan Deng. WWW 2021.

* [TSA: A Truthful Mechanism for Social Advertising](10.1145/3336191.3371809) by Tobias Grubenmann. WSDM 2020.

* [Optimal Auctions through Deep Learning](http://arxiv.org/abs/1706.03459) by Paul Dütting. ArXiv 2020. 

* [Truthfulness with Value-Maximizing Bidders: On the Limits of Approximation in Combinatorial Markets](http://dss.in.tum.de/files/bichler-research/2016_fadaei_bichler_value_bidders.pdf) by Salman Fadaei and Martin Bichler. EJOR 2016.

* [Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design](http://wnzhang.net/share/rtb-papers/repeat-auction.pdf) by Santiago R. Balseiro, Omar Besbesy, Gabriel Y. Weintraub. Management Science 2015.

* [Ad Exchange: Intention Driven Auction Mechanisms for Mediating Between Publishers and Advertisers](http://wnzhang.net/share/rtb-papers/adx-mec.pdf) by Rina Azoulay, Esther David. WI/IAT 2015.

* [Pricing Externalities in Real-Time Bidding Markets](http://wnzhang.net/share/rtb-papers/rtb-pricing-ext.pdf) by Joseph Reisinger, Michael Driscoll. Machine Learning in Online Advertising.

* [Competition between Demand-Side Intermediaries in Ad Exchanges](http://wnzhang.net/share/rtb-papers/dsp-comp.pdf) by Lampros C. Stavrogiannis. PhD Thesis 2014.

* [Auction Mechanisms for Demand-Side Intermediaries in Online Advertising Exchanges](http://wnzhang.net/share/rtb-papers/auc-mec-dsp.pdf) by Lampros C. Stavrogiannis, Enrico H. Gerding, Maria Polukarov. AMMAS 2014.

* [Optimal Revenue-Sharing Double Auctions with Applications to Ad Exchanges](http://wnzhang.net/share/rtb-papers/double-auc-adx.pdf) by Renato Gomes, Vahab Mirrokni. WWW 2014.

* [Competition and Yield Optimization in Ad Exchanges](http://wnzhang.net/share/rtb-papers/yield-opt-adx.pdf) by Santiago R. Balseiro. PhD Thesis 2013.

* [Selective Call Out and Real Time Bidding](http://wnzhang.net/share/rtb-papers/select-callout.pdf) by Tanmoy Chakraborty. WINE 2010.



## Privacy

* [Selling Off Privacy at Auction](http://wnzhang.net/share/rtb-papers/privacy.pdf) by Lukasz Olejnik, Tran Minh-Dung, Claude Castelluccia. NDSS 2014.

* [Network Analysis of Third Party Tracking: User Exposure to Tracking Cookies through Search](http://wnzhang.net/share/rtb-papers/user-tracking.pdf) by Richard Gomer et al. WI 2013.



## Systems

* [Finding Needle in a Million Metrics: Anomaly Detection in a Large-scale Computational Advertising Platform](http://arxiv.org/pdf/1602.07057.pdf) by Bowen Zhou, Shahriar Shariat. TargetAd 2016.



## Datasets and Benchmarking

* [YOYI RTB datasets (with bidding information)](http://apex.sjtu.edu.cn/datasets/7) by Kan Ren and Yifei Rong et al. CIKM 2016.

* [iPinYou Global RTB Bidding Algorithm Competition Dataset](http://wnzhang.net/share/rtb-papers/ipinyou-dataset.pdf) by Hairen Liao et al. ADKDD 2014.

* [Real-Time Bidding Benchmarking with iPinYou Dataset](http://arxiv.org/abs/1407.7073) by Weinan Zhang et al. ArXiv 2014.

* [Criteo Dataset for Product Recommendation / Counterfactual Learning](http://www.cs.cornell.edu/~adith/Criteo/NIPS16_Benchmark.pdf) by Damien Lefortier et al. What If workshop NIPS 2016.

* [Criteo Conversion Logs Dataset](http://labs.criteo.com/2013/12/conversion-logs-dataset/) by Criteo Labs.

* [Criteo Terabyte Click Logs](http://labs.criteo.com/downloads/download-terabyte-click-logs/) by Criteo Labs.

* [Avazu Click Prediction](https://www.kaggle.com/c/avazu-ctr-prediction/data) by Avazu.