Foundations and Trends® in Information Retrieval > Vol 11 > Issue 4-5

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting

Jun Wang, (University College London, UK, junwang@cs.ucl.ac.uk Weinan Zhang, Shanghai Jiao Tong University, China, wnzhang@sjtu.edu.cn Shuai Yuan, MediaGamma Ltd. London, UK, shuai.yuan@mediagamma.com
 
Suggested Citation
Jun Wang, Weinan Zhang and Shuai Yuan (2017), "Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting", Foundations and Trends® in Information Retrieval: Vol. 11: No. 4-5, pp 297-435. http://dx.doi.org/10.1561/1500000049

Published: 24 Jul 2017
© 2017 J. Wang, W. Zhang and S. Yuan
 
Subjects
Applications of IR,  User modelling and user studies for IR,  Collaborative filtering and recommender systems,  Economics of information and the Web,  Data Mining
 

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In this article:
1. Introduction
2. How RTB Works
3. RTB Auction Mechanism and Bid Landscape Forecasting
4. User Response Prediction
5. Bidding Strategies
6. Dynamic Pricing
7. Attribution Models
8. Fraud Detection
9. The Future of RTB
A. RTB Glossary
References

Abstract

The most significant progress in recent years in online display advertising is what is known as the Real-Time Bidding (RTB) mechanism to buy and sell ads. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large amount of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimisation in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields. In this monograph, an overview is given of the fundamental infrastructure, algorithms, and technical solutions of this new frontier of computational advertising. The covered topics include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimisation, statistical arbitrage, dynamic pricing, and ad fraud detection.

DOI:10.1561/1500000049
ISBN: 978-1-68083-310-2
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ISBN: 978-1-68083-309-6
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Table of contents:
1. Introduction
2. How RTB Works
3. RTB Auction Mechanism and Bid Landscape Forecasting
4. User Response Prediction
5. Bidding Strategies
6. Dynamic Pricing
7. Attribution Models
8. Fraud Detection
9. The Future of RTB
A. RTB Glossary
References

Display Advertising with Real-Time Bidding (RTB) and Behavioural Targetin

Online advertising is now one of the fastest advancing areas in the IT industry. In display and mobile advertising, the most significant technical development in recent years is the growth of Real-Time Bidding (RTB), which facilitates a real-time auction for a display opportunity. RTB essentially facilitates buying an individual ad impression in real time while it is still being generated from a user’s visit. RTB not only scales up the buying process by aggregating a large number of available inventories across publishers but, most importantly, enables direct targeting of individual users. As such, RTB has fundamentally changed the landscape of digital marketing. Scientifically, the demand for automation, integration and optimization in RTB also brings new research opportunities in information retrieval, data mining, machine learning and other related fields.

Despite its rapid growth and huge potential, many aspects of RTB remain unknown to the research community for a variety of reasons. This monograph offers insightful knowledge of real-world systems, to bridge the gaps between industry and academia, and to provide an overview of the fundamental infrastructure, algorithms, and technical and research challenges of the new frontier of computational advertising. The topics covered include user response prediction, bid landscape forecasting, bidding algorithms, revenue optimization, statistical arbitrage, dynamic pricing, and ad fraud detection.

This is an invaluable text for researchers and practitioners alike. Academic researchers will get a better understanding of the real-time online advertising systems currently deployed in industry. While industry practitioners are introduced to the research challenges, the state of the art algorithms and potential future systems in this field.

 
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