Online advertising, as the vast market, has gained significant attention in various platforms ranging from search engines, third-party websites, social media, and mobile apps. The prosperity of online campaigns is a challenge in online marketing and is usually evaluated by user response through different metrics, such as clicks on advertisement (ad) creatives, subscriptions to products, purchases of items, or explicit user feedback through online surveys. Recent years have witnessed a significant increase in the number of studies using computational approaches, including machine learning methods, for user response prediction. However, existing literature mainly focuses on algorithmic-driven designs to solve specific challenges, and no comprehensive review exists to answer many important questions. What are the parties involved in the online digital advertising eco-systems? What type of data are available for user response prediction? How to predict user response in a reliable and/or transparent way? In this survey, we provide a comprehensive review of user response prediction in online advertising and related recommender applications. Our essential goal is to provide a thorough understanding of online advertising platforms, stakeholders, data availability, and typical ways of user response prediction. We propose a taxonomy to categorize state-of-the-art user response prediction methods, primarily focus on the current progress of machine learning methods used in different online platforms. In addition, we also review applications of user response prediction, benchmark datasets, and open-source codes in the field.
翻译:在线广告作为广阔的市场,在搜索引擎、第三方网站、社交媒体和移动应用程序等各种平台上引起了人们的极大关注。在线运动的繁荣是在线营销的一个挑战,通常由用户通过不同衡量标准,例如点击广告(ad)创意、产品订阅、项目采购或通过在线调查提供明确的用户反馈等,对在线广告作出评价。近年来,利用计算方法,包括机器学习方法进行用户响应预测的研究数量显著增加。然而,现有文献主要侧重于应对具体挑战的算法驱动设计,没有进行全面审查来回答许多重要问题。在线数字广告生态系统中的参与者是什么?用户反应预测可用何种类型的数据?如何以可靠和(或)透明的方式预测用户的反应?在本调查中,我们全面审查了在线广告和相关建议应用程序中的用户反应预测。我们的基本目标是提供对在线广告平台、利益攸关方、数据提供和用户反应预测的典型方法的透彻理解。我们提议在目前用户预测平台应用的状态、使用的最新用户预测方法中进行分类,并主要侧重于当前用户预测的在线预测方法。