User Response Prediction in Online Advertising
User Response Prediction in Online Advertising
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DOI：
10.1145/3446662
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发表时间：
2022-04-01
影响因子：
16.6
通讯作者：
Zhu，Xingquan
中科院分区：
文献类型：
--
作者：
Gharibshah，Zhabiz;Zhu，Xingquan
在线广告作为一个广阔的市场，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获得了极大的关注。在线活动的繁荣是在线营销中的一个挑战，通常通过不同指标的用户响应来评估，例如广告创意的点击次数、产品订阅、物品购买或通过在线调查获得的明确用户反馈。近年来，使用计算方法（包括机器学习方法）进行用户响应预测的研究数量显着增加。然而，现有文献主要关注算法驱动的设计来解决特定的挑战，并且没有全面的综述来回答许多重要的问题。在线数字广告生态系统涉及哪些各方？哪些类型的数据可用于用户响应预测？我们如何以可靠和/或透明的方式预测用户响应？在本次调查中，我们对在线广告和相关推荐应用程序中的用户响应预测进行了全面审查。我们的基本目标是全面了解在线广告平台、利益相关者、数据可用性和用户响应预测的典型方法。我们提出了一种分类法来对最先进的用户响应预测方法进行分类，主要关注不同在线平台中使用的机器学习方法的当前进展。此外，我们还回顾了用户响应预测、基准数据集和开源代码在该领域的应用。
Online advertising, as a 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 do we 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 focusing 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.