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.

近年来，在线展示广告中最重要的进步就是所谓的实时出价（RTB）机制，用于买卖广告。实时出价实质上有助于实时购买单个广告展示，而仍然是通过用户的访问而产生的。实时出价不仅可以通过汇总发布商之间的大量可用库存来扩大购买过程，而且最重要的是，它可以直接定位单个用户。因此，实时出价从根本上改变了数字营销的格局。从科学上讲，实时出价工具对自动化，集成和优化的需求也为信息检索，数据挖掘，机器学习和其他相关领域带来了新的研究机会。本专论概述了基础架构，算法，和计算广告这一新领域的技术解决方案。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。