  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.
