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The iSOCRATES Reading, Viewing and Listening List
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www.isocrates.com, www.charleswarner.us, www.adexchanger.com, www.exchangewire.com, www.mediapost.com, www.thedrum.com, www.digiday.com, www.adage.com, www.adweek.com, www.emarketer.com, www.warc.com, www.wsj.com/news/types/cmo, www.martech.com, www.martechadvisor.com, https://chiefmartec.com/
adexchanger, mediapost, digiday, WARC, emarketer, thedrum, marketingprofs, martechadvisor, iSOCRATES
Advertising Week (Fall), Ad:tech (Fall), Digiday (almost monthly), MediaPost (almost monthly), MarTech (Fall), dmexco (Fall), iabALM (Winter)
200MS: The Life of a MADTech RTB Ad Impression, William Lederer for MediaCrossing, 2013 https://www.youtube.com/watch?v=rTg9l4d8MU4
MADTech 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:
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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.
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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.
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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 MADTech 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.
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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.
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