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

By Jun Wang, Weinan Zhang, Shuai Yuan. 2016

222 citations recorded by the source corpus.

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 users 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.

Subfields: Computer Science Applications, Management Science and Operations Research, Marketing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Weinan Zhang
2. Jun Wang
3. Yaodong Yang
4. Ying Wen

In Management Science and Operations Research

1. Weinan Zhang
2. Yaodong Yang
3. M. Zhou
4. Han Cai
5. Yong Yu
6. Quan Yuan
7. Peng Peng
8. Jun Wang

In Marketing

1. Weinan Zhang
2. Yong Yu
3. Jun Wang

Citations in the corpus

• Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games (2017, background): “ence start to emerge from multiple domains. Typical examples include the trading robots gaming on the stock markets [13], ad bidding agents competing with each other over online advertising exchanges [14], and e-commerce collaborative filtering recommenders [15] predicting user interests through the ”
• Mean Field Multi-Agent Reinforcement Learning (2018, background): “…in practice, many cases do require strategic interactions among a large number of agents, such as the gaming bots in Massively Multiplayer Online Role-Playing Game (Jeong et al., 2015), the trading agents in stock markets (Troy, 1997), or the online advertising bidding agents (Wang et al., 2017).”
• Real-Time Bidding by Reinforcement Learning in Display Advertising (2017, background): “A recent comprehensive study on the data science of RTB display advertising is posted in [24].”
• Multiagent Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat Games (2017, background): “Typical examples include the trading robots gaming on the stock markets [13], ad bidding agents competing with each other over online advertising exchanges [14], and e-commerce collaborative filtering recommenders [15] predicting user interests through the wisdom of the crowd [16].”