User Response Prediction in Online Advertising

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 challenge marketing usually evaluated by user response through different metrics, such clicks on advertisement (ad) creatives, subscriptions to products, purchases items, or explicit feedback surveys. Recent years have witnessed increase the number studies using computational approaches, including machine learning methods, for prediction. However, existing literature mainly focuses algorithmic-driven designs solve specific challenges, no comprehensive review exists answer many important questions. What are parties involved digital advertising eco-systems? type data available prediction? How do we predict reliable and/or transparent way? In this survey, provide prediction related recommender applications. Our essential goal thorough understanding platforms, stakeholders, availability, typical ways We propose taxonomy categorize state-of-the-art primarily focusing current progress methods used platforms. addition, also applications prediction, benchmark datasets, open source codes field.