What We are Reading and Viewing

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Conferences:

Advertising Week (Fall), Ad:tech (Fall), Digiday (almost monthly), MediaPost (almost monthly), MarTech (Fall), dmexco (Fall), iabALM (Winter)

 

Videos:

200MS: The Life of a Programmatic RTB Ad Impression, William Lederer for MediaCrossing, 2013 https://www.youtube.com/watch?v=rTg9l4d8MU4

 

Books:

Programmatic Advertising: The Successful Transformation to Automated, Data-Driven Marketing in Real-: How Time, Oliver Busch Editor, Springer, 2015; Targeted: How Technology is Revolutionizing Advertising and The Way Companies Reach Consumers, Mike Smith, Amacom, 2015; The Rise of the Platform Marketer: Performance Marketing with Google, Facebook, Twitter, Plus the Latest High-Growth Digital Advertising Platforms, Craig Dempster and John Lee, Wiley, 2015; Competing on Analytics: Updated, with a New Introduction: The New Science of Winning, Thomas H. Davenport and Jeanne Harris, Harvard Business Review Press, 2017; Digital Marketing Analytics: Making Sense of Consumer Data in a Digital World, Chuck Hemann and Ken Burbary, Que, 2013; Internet Ad Pioneers, Cory Treffiletti, Self-published, 2013; Ad Serving Technology, Gregory Cristal, Self-Published, 2014; Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting, Jun Wang, Weinan Zhang, Shuai Yuan, now Publishers, 2016; The Media Handbook, 7th Edition, Helen Katz, Routledge, 2020; AI for Marketing and Product Innovation, A.K. Pradeep, Andrew Appel, Stan Sthanunathan, Wiley, 2020; AI in Marketing Sales and Service, Peter Gentsch, Palgrave Macmillan, 2020; Hands-On Data Science for Marketing, Yoon Hyup Hwang, 2020; Introduction to Algorithmic Marketing, Ilya Katsov, 2018; Media Selling, 5th Edition, Charles Warner, William Lederer, and Brian Moroz, Wiley, 2020.

Computational Advertising Materials:

Vibhanshu Abhishek, Peter Fader, and Kartik Hosanagar. Media exposure through the funnel: A model of multi-stage attribution. Available at SSRN2158421, 2012.

 

Zoë Abrams. Revenue maximization when bidders have budgets. In SODA, pages 1074–1082, 2006.

 

Gunes Acar, Christian Eubank, Steven Englehardt, Marc Juarez, Arvind Narayanan, and Claudia Diaz. The web never forgets: Persistent tracking mechanisms in the wild. In Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security , pages 674–689. ACM,2014.

 

Deepak Agarwal, Souvik Ghosh, Kai Wei, and Siyu You. Budget pacing for targeted online advertisements at linkedin. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1613–1619. ACM, 2014.

 

Rakesh Agrawal, Sreenivas Gollapudi, Alan Halverson, and Samuel Ieong. Diversifying search results. In Proceedings of the Second ACM International Conference on Web Search and Data Mining, pages 5–14. ACM, 2009.

 

Amr Ahmed, Yucheng Low, Mohamed Aly, Vanja Josifovski, and Alexander J Smola. Scalable distributed inference of dynamic user interests for behavioral targeting. In KDD, 2011.

 

Amr Ahmed, Abhimanyu Das, and Alexander J Smola. Scalable hierarchical multitask learning algorithms for conversion optimization in display advertising. In Proceedings of the 7th ACM international conference on Web search and data mining, pages 153–162. ACM, 2014.

 

Sumayah A. Alrwais, Alexandre Gerber, Christopher W. Dunn, Oliver Spatscheck, Minaxi Gupta, and Eric Osterweil. Dissecting ghost clicks: Ad fraud via misdirected human clicks. In Proceedings of the 28th Annual Computer Security Applications Conference, ACSAC ’12, pages 21–30, New York, NY, USA, 2012. ACM. ISBN 978-1-4503-1312-4. URL http://doi.acm.org/10.1145/2420950.2420954.

 

Kareem Amin, Michael Kearns, Peter Key, and Anton Schwaighofer. Budget optimization for sponsored search: Censored learning in mdps. In UAI, 2012.

 

Aris Anagnostopoulos, Andrei Z. Broder, Evgeniy Gabrilovich, Vanja Josifovski, and Lance Riedel. Just-in-time contextual advertising. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management, CIKM ’07, pages 331–340, New York, NY, USA, 2007. ACM. ISBN 978-1-59593-803-9. URL http://doi.acm.org/10.1145/1321440.1321488.

 

Eva Anderl, Ingo Becker, Florian VWangenheim, and Jan Hendrik Schumann. Mapping the customer journey: A graph-based framework for online attribution modeling. Available at SSRN 2343077, 2014.

 

Animesh Animesh, Vandana Ramachandran, and Siva Viswanathan. Online Advertisers Bidding Strategies for Search, Experience, and Credence Goods: An Empirical Investigation. In Second Workshop on Sponsored Search Auctions. EC, 2005. URL http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.114.5347&rep=rep1&type=pdf.

 

Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multiarmed bandit problem. Machine learning, 47 (2-3):235–256, 2002.

 

Ricardo Baeza-Yates, Berthier Ribeiro-Neto, et al. Modern information retrieval, volume 463. ACM press New York, 1999.

 

Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473, 2014.

 

Santiago Balseiro and Ozan Candogan. Optimal contracts for intermediaries in online advertising. Available at SSRN 2546609, 2015.

 

Santiago R Balseiro, Omar Besbes, and Gabriel Y Weintraub. Repeated auctions with budgets in ad exchanges: Approximations and design. Management Science, 61 (4):864–884, 2015. BBC. Google to charge advertisers viewed for seen ads. http://www.bbc.com/news/business-25356956, 2013. Accessed: 2016-07.

 

Yoshua Bengio, Pascal Lamblin, Dan Popovici, Hugo Larochelle, et al. Greedy layer-wise training of deep networks. Advances in neural information processing systems, 19:153, 2007.

 

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Károly Boda, Ádám Máté Földes, Gábor György Gulyás, and Sándor Imre. User tracking on the web via cross-browser fingerprinting. In Nordic Conference on Secure IT Systems, pages 31–46. Springer, 2011.

 

Christian Borgs, Jennifer Chayes, Nicole Immorlica, Kamal Jain, Omid Etesami, and Mohammad Mahdian. Dynamics of bid optimization in online advertisement auctions. In Proceedings of the 16th international conference on World Wide Web, pages 531–540. ACM, 2007.

 

Leo Breiman. Bagging predictors. Machine learning, 24 (2):123–140, 1996.

 

Leo Breiman. Random forests. Machine learning, 45 (1):5–32, 2001.

 

Leo Breiman, Jerome Friedman, Charles J Stone, and Richard A Olshen. Classification and regression trees. CRC press, 1984.

 

Andrei Broder, Marcus Fontoura, Vanja Josifovski, and Lance Riedel. A semantic approach to contextual advertising. In Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR ’07, pages 559–566, New York, NY, USA, 2007. ACM. ISBN 978-1-59593-597-7. URL http://doi.acm.org/10.1145/1277741.1277837.

 

Andrei Broder, Evgeniy Gabrilovich, Vanja Josifovski, George Mavromatis, and Alex Smola. Bid generation for advanced match in sponsored search. In WSDM, pages 515–524. ACM, 2011. Interactive Advertising Bureau. What is an untrustworthy supply chain costing the us digital advertising industry? 2015.

 

Nicolo Cesa-Bianchi, Claudio Gentile, and Yishay Mansour. Regret minimization for reserve prices in second-price auctions. In Proceedings of the Twenty-Fourth Annual ACM-SIAM Symposium on Discrete Algorithms, pages 1190–1204. Society for Industrial and Applied Mathematics, 2013.

 

Tanmoy Chakraborty, Eyal Even-Dar, Sudipto Guha, Yishay Mansour, and S Muthukrishnan. Selective call out and real time bidding. In Internet and Network Economics, pages 145–157. 2010.

 

Olivier Chapelle. Modeling delayed feedback in display advertising. In Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1097–1105. ACM, 2014.

 

Olivier Chapelle. Offline evaluation of response prediction in online advertising auctions. WWW, 2015.

 

Olivier Chapelle, Eren Manavoglu, and Romer Rosales. Simple and scalable response prediction for display advertising. ACM Transactions on Intelligent Systems and Technology (TIST) , 5 (4):61, 2014.

 

Bowei Chen and Jun Wang. A lattice framework for pricing display ad options with the stochastic volatility underlying model. arXiv preprint arXiv:1409.0697, 2014.

 

Bowei Chen, Jun Wang, Ingemar J Cox, and Mohan S Kankanhalli. Multikeyword multi-click option contracts for sponsored search advertising. CoRR , 2013.

 

Bowei Chen, Shuai Yuan, and Jun Wang. A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, ADKDD’14, pages 1:1–1:9, New York, NY, USA, 2014. ACM. ISBN 978-1-4503-2999-6. URL http://doi.acm.org/10.1145/2648584.2648585.

 

Tianqi Chen, Zhao Zheng, Qiuxia Lu, Weinan Zhang, and Yong Yu. Featurebased matrix factorization. arXiv preprint arXiv:1109.2271, 2011a.

 

Ye Chen, Pavel Berkhin, Bo Anderson, and Nikhil R Devanur. Real-time bidding algorithms for performance-based display ad allocation. In Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining, pages 1307–1315. ACM, 2011b.

 

Charles LA Clarke, Maheedhar Kolla, Gordon V Cormack, Olga Vechtomova, Azin Ashkan, Stefan Büttcher, and Ian MacKinnon. Novelty and diversityin information retrieval evaluation. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, pages 659–666. ACM, 2008.

 

Ronan Collobert and Jason Weston. A unified architecture for natural language processing: Deep neural networks with multitask learning. In Proceedings of the 25th international conference on Machine learning, pages 160–167. ACM, 2008.

 

Jonathan Crussell, Ryan Stevens, and Hao Chen. Madfraud: Investigating ad fraud in android applications. In Proceedings of the 12th annual international conference on Mobile systems, applications, and services, pages 123–134. ACM, 2014.

 

Ying Cui, Ruofei Zhang, Wei Li, and Jianchang Mao. Bid landscape forecasting in online ad exchange marketplace. In KDD, pages 265–273. ACM, 2011.

 

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Wenyuan Dai, Gui-Rong Xue, Qiang Yang, and Yong Yu. Transferring naïve bayes classifiers for text classification. In AAAI, 2007.

 

Brian Dalessandro, Rod Hook, Claudia Perlich, and Foster Provost. Evaluating and optimizing online advertising: Forget the click, but there are good proxies. 2012a.

 

Brian Dalessandro, Claudia Perlich, Ori Stitelman, and Foster Provost. Causally motivated attribution for online advertising. In Proceedings of the Sixth International Workshop on Data Mining for Online Advertising and Internet Economy, page 7. ACM, 2012b.

 

Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, and Foster Provost. Scalable hands-free transfer learning for online advertising. In Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’14, pages 1573–1582, New York, NY, USA, 2014a. ACM. ISBN 978-1-4503-2956-9. URL http://doi.acm.org/10.1145/2623330.2623349.

 

Brian Dalessandro, Daizhuo Chen, Troy Raeder, Claudia Perlich, Melinda Han Williams, and Foster Provost. Scalable hands-free feature learning for online advertising. In KDD, 2014b.

 

Neil Daswani, Chris Mysen, Vinay Rao, Stephen Weis, Kourosh Gharachorloo, and Shuman Ghosemajumder. Online advertising fraud. Crimeware: understanding new attacks and defenses, 40 (2):1–28, 2008.

 

Li Deng, Xiaodong He, and Jianfeng Gao. Deep stacking networks for information retrieval. In 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pages 3153–3157. IEEE, 2013.

 

Nikhil R Devenur and Thomas P Hayes. The adwords problem: online keyword matching with budgeted bidders under random permutations. In EC, pages 71–78. ACM, 2009.

 

Josh Dreller. A brief history of paid search advertising. http://searchengineland.com/a-brief-history-of-paid-search-advertising-33792 (last visited 13/12/2011), 2010.

 

Michael Durbin. All about high-frequency trading. McGraw Hill Professional, 2010.

 

Peter Eckersley. How unique is your web browser? In International Symposium on Privacy Enhancing Technologies Symposium, pages 1–18. Springer, 2010.

 

Benjamin Edelman and Michael Schwarz. Optimal auction design in a multi-unit environment: The case of sponsored search auctions. Unpublished manuscript, Harvard Business School, 2006. URL http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Optimal+Auction+Design+in+a+Multi-unit+Environment+:+The+Case+of+Sponsored+Search+Auctions#0.

 

Benjamin Edelman, Michael Ostrovsky, and Michael Schwarz. Internet advertising and the generalized second price auction: Selling billions of dollars worth of keywords. Technical report, National Bureau of Economic Research, 2005.

 

Eyal Even-Dar, Jon Feldman, Yishay Mansour, and S. Muthukrishnan. Position auctions with bidder-specific minimum prices. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5385 LNCS:577–584, 2008. ISSN 03029743.

 

Eyal Even Dar, Vahab S Mirrokni, S Muthukrishnan, Yishay Mansour, and Uri Nadav. Bid optimization for broad match ad auctions. In WWW, pages 231–240. ACM, 2009.

 

Maryam Feily, Alireza Shahrestani, and Sureswaran Ramadass. A survey of botnet and botnet detection. In 2009 Third International Conference on Emerging Security Information, Systems and Technologies, pages 268–273. IEEE, 2009.

 

Jon Feldman, S Muthukrishnan, Martin Pal, and Cliff Stein. Budget optimization in search-based advertising auctions. In EC, 2007.

 

Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The elements of statistical learning, volume 1. Springer series in statistics Springer, Berlin, 2001.

 

Jerome H Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001.

 

Jerome H Friedman. Stochastic gradient boosting. Computational Statistics & Data Analysis, 38 (4):367–378, 2002.

 

Gian M Fulgoni. Fraud in digital advertising: A multibillion-dollar black hole. Journal of Advertising Research, pages JAR–2016, 2016.

 

Sahin Cem Geyik, Abhishek Saxena, and Ali Dasdan. Multi-touch attribution based budget allocation in online advertising. In Proceedings of 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 1–9. ACM, 2014.

 

Arpita Ghosh, Benjamin I P Rubinstein, Sergei Vassilvitskii, and Martin Zinkevich. Adaptive bidding for display advertising. In WWW , 2009. ISBN 9781605584874.

 

Richard Gomer, Eduarda Mendes Rodrigues, Natasa Milic-Frayling, and MC Schraefel. Network analysis of third party tracking: User exposure to tracking cookies through search. In Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013 IEEE/WIC/ACM International Joint Conferences on, volume 1, pages 549–556. IEEE, 2013. Google. Ad traffic quality resource center. http://www.google.com/ads/adtrafficquality/index.html. Last visited on 04/07/2016.

 

Jagadeesh Gorla, Neal Lathia, Stephen Robertson, and Jun Wang. Probabilistic group recommendation via information matching. In Proceedings of the 22nd international conference on World Wide Web, pages 495–504. ACM, 2013.

 

JY Gorla. A bi-directional unified Model for information retrieval . PhD thesis, UCL (University College London), 2016.

 

Thore Graepel, Joaquin Q Candela, Thomas Borchert, and Ralf Herbrich. Web-scale bayesian click-through rate prediction for sponsored search advertising in microsoft’s bing search engine. In Proceedings of the 27th International Conference on Machine Learning (ICML-10) , pages 13–20, 2010.

 

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William H Greene. Censored data and truncated distributions. Available at SSRN 825845, 2005.

 

R Gummadi, PB Key, and A Proutiere. Optimal bidding strategies in dynamic auctions with budget constraints. In CCC, pages 588–588. IEEE, 2011.

 

Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In Proceedings of the IEEE International Conference on Computer Vision, pages 1026–1034, 2015.

 

Xinran He, Junfeng Pan, Ou Jin, Tianbing Xu, Bo Liu, Tao Xu, Yanxin Shi, Antoine Atallah, Ralf Herbrich, Stuart Bowers, et al. Practical lessons from predicting clicks on ads at facebook. In Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, pages 1–9. ACM, 2014.

 

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Bernard J. Jansen. Sponsored Search: Is Money a Motivator for Providing Relevant Results. IEEE Computer, 2007.

 

Thorsten Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pages 133–142. ACM, 2002.

 

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Yuchin Juan, Yong Zhuang, Wei-Sheng Chin, and Chih-Jen Lin. Field-aware factorization machines for ctr prediction. 2016.

 

Ren Kan,Weinan Zhang, Yifei Rong, Haifeng Zhang, Yong Yu, and JunWang. User response learning for directly optimizing campaign performance in display advertising. In Proceedings of the ACM international conference on Conference on information & knowledge management. ACM, 2016.

 

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Kuang-Chih Lee, Ali Jalali, and Ali Dasdan. Real time bid optimization with smooth budget delivery in online advertising. ADKDD, 2013a. URL http://dl.acm.org/citation.cfm?doid=2501040.2501979.

 

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