This website is for the WSDM 2015 Tutorial: Real-Time Bidding: A New Frontier of Computational Advertising Research.
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In display and mobile advertising, the most significant development in recent years is the Real-Time Bidding (RTB), which allows selling and buying in real-time one ad impression at a time. Since then, RTB has fundamentally changed the landscape of the digital marketing by scaling the buying process across a large number of available inventories. The demand for automation, integration and optimisation in RTB brings new research opportunities in the IR/DM/ML fields. However, despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for many reasons. In this tutorial, together with invited distinguished speakers from online advertising industry, we aim to bring the insightful knowledge from the real-world systems to bridge the gaps and provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of this new frontier of computational advertising. We will also introduce to researchers the datasets, tools, and platforms which are publicly available thus they can get hands-on quickly.
This tutorial aims to provide not only a comprehensive and systematic introduction to RTB and computational advertising in general, but also the emerging research challenges and research tools and datasets in order to facilitate the research. Compared to previous Computational Advertising tutorials in relevant top-tier conferences, this tutorial takes a fresh, neutral, and the latest look of the field and focuses on the fundamental changes brought by RTB. We expect the audience, after attending the tutorial, to understand the real-time online advertising mechanisms and the state of the art techniques, as well as to grasp the research challenges in this field. Our motivation is to help the audience acquire domain knowledge and obtain relevant datasets, and to promote research activities in RTB and computational advertising in general.
The background of Computational advertising
Research problems and techniques
Bidding strategy Optimisation
Inventory management and reserve price optimisation
Fighting publisher fraud
Programmatic Guaranteed and Ad Options
Datasets, tools, and platforms
Dr. Jun Wang is a Senior Lecturer (Associate Professor) in University College London. He has published over 70 research papers in leading journals and conference proceedings including ACM Trans. on Information Systems, IEEE Trans. on Multimedia, ACM Multimedia System Journal, WWW, CIKM, ACM SIGIR, SIGMM. He received the Best Doctoral Consortium award in ACM SIGIR06 for his work on collaborative filtering, the Best Paper Prize in ECIR09 for his work on applying Modern Portfolio Theory of Finance (Mean-variance Analysis) to document ranking in Information Retrieval, and the Best Paper Prize in ECIR12 for top-k retrieval modelling. He has extensive experiences in giving tutorials on top conferences: his recent tutorials about risk management and portfolio theory of information retrieval were given in CIKM2011 and ECIR2011. Dr. Jun Wang has also delivered the following tutorials:
ECIR 2011, Risk Management in Information Retrieval.
CIKM 2011, Statistical Information Retrieval Modelling: From Probability Ranking Principle to recent advances in diversity, Portfolio Theory, and beyond.
SIGIR 2013, Dynamic IR Modelling.
Dr. Shuai Yuan recently received his Ph.D. from University College London. He has been working on mathematical models of online advertising with a number of companies such as AppNexus, Advance International Media, Bright, Dot.tk, and Miaozhen. He has the background of information retrieval, data mining, machine learning, and economic theories; his research interests on computational advertising have focused on supply side optimisation in RTB, bidding algorithms, and statistical arbitrage. Shuai Yuan has published several papers in top-tier venues including CIKM, SIGKDD, and ADKDD. Among them, he published the first empirical study on RTB auctions. He and his colleagues won the third season of iPinyou Global Bidding Algorithm Competition in 2013, and the Best Paper Award of ADKDD 2014. He also contributes to an open advertising dataset project.
Dr. Kaihua Cai is the invited speaker of the tutorial. He received Ph.D. in Mathematics from Caltech, focusing on harmonic analysis and partial differential equations. After graduation, He was a research fellow at MSRI in Berkeley, California and Institute for Advanced Study in Princeton, New Jersey. Before joining AppNexus as a data scientist in 2012, he worked in finance. Specifically, he was employed at Chatham Financial in Philadelphia, Goral Trading in Baltimore and IV Capital in New York City.
AppNexus is one of the largest online advertising exchanges. It offers one of the most powerful, open and customizable advertising technology platforms for both the buy and sell sides. It serves Google AdX, Microsoft Advertising Exchange, Interative Media (Deutsche Telekom), Collective Exchange, and a lot more.
CIKM 2009, IJCAI 2009, EC 2008, and ACL-HLT 2008, Introduction to Computational Advertising, Andrei Broder, Vanja Josifovski, and Evgeniy Gabrilovich.
KDD 2009, Statistical Challenges in Computational Advertising, Deepayan Chakrabarti and Deepak Agarwal.
SIGIR 2010, CIKM 2011, Information Retrieval Challenges in Computational Advertising, Andrei Broder, Evgeniy Gabrilovich, and Vanja Josifovski.