Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting
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DOI：
10.1561/1500000049
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发表时间：
2016-10
期刊：
影响因子：
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通讯作者：
Jun Wang;Weinan Zhang;Shuai Yuan
中科院分区：
文献类型：
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作者：
Jun Wang;Weinan Zhang;Shuai Yuan
在线广告现在是IT行业发展最快的领域之一。在显示和移动的广告中，近年来最显著的技术发展是实时竞价（RTB）的增长，其促进了针对显示机会的实时拍卖。RTB本质上促进了在真实的时间内购买个人广告印象，而它仍然是从用户访问中生成的。RTB不仅通过聚合出版商之间的大量可用库存来扩展购买过程，而且最重要的是，可以直接针对个人用户。因此，RTB从根本上改变了数字营销的格局。科学性、自动化、集成化和优化的需求也为信息检索、数据挖掘、机器学习等相关领域带来了新的研究机会。尽管RTB发展迅速，潜力巨大，但由于各种原因，RTB的许多方面仍然不为研究界所知。这本专著提供了对现实世界系统的深刻了解，弥合了工业界和学术界之间的差距，并概述了计算广告新前沿的基本基础设施，算法以及技术和研究挑战。涵盖的主题包括用户响应预测，出价前景预测，出价算法，收入优化，统计套利，动态定价和广告欺诈检测。这是一个宝贵的文本研究人员和从业人员一样。学术研究人员将更好地了解目前在行业中部署的实时在线广告系统。虽然行业从业者介绍了研究的挑战，最先进的算法和潜在的未来系统在这一领域。
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.