跳至正文
返回文库全部文档

实时竞价系统与行为广告定向算法

文章 arXiv papers · 作者: Jun Wang et al.
阅读原文

总结

本文介绍实时竞价（RTB），即在用户访问网站期间买卖单个展示广告曝光的方式。通过汇总发布商的广告库存并针对每次曝光作出决策，RTB支持自动化购买和面向特定用户的定向投放。

文章概述了计算广告领域的多个方向，包括用户响应预测、竞价环境预测、出价与收益优化、统计套利、动态定价和广告欺诈检测。这些主题将数据挖掘和机器学习与市场设计及自动化决策联系起来。本文是概述而非详细分析：没有提供算法、实证结果或交易市场应用，因此与量化交易的相关性较为间接。

核心观点

  • RTB 在用户访问发布商网站时实时分配单个广告曝光。
  • 汇总多个发布商的广告库存，使买方能够大规模自动购买广告。
  • 行为定向使用个人用户信息来指导每次曝光的广告决策。
  • 相关技术问题包括响应预测、竞价预测、收益优化和欺诈检测。

标签

机器学习统计学套利
全文
# Display Advertising with Real-Time Bidding (RTB) and Behavioural Targeting


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









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。