Predicting the probability that a user will click on a specific advertisement has been a
prevalent issue in online advertising, attracting much research attention in the past decades …

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 …

Click-through rate (CTR) prediction is a critical task for many applications, as its accuracy
has a direct impact on user experience and platform revenue. In recent years, CTR …

Click-through rate (CTR) prediction is a crucial task in recommender systems and online
advertising. The embedding-based neural networks have been proposed to learn both …

Learning effective high-order feature interactions is very crucial in the CTR prediction task.
However, it is very time-consuming to calculate high-order feature interactions with massive …

This work conducts a systematic literature review about the domain of personalized
advertisement, and more specifically, about the techniques that are used for this purpose …

A domain that has gained popularity in the past few years is personalized advertisement.
Researchers and developers collect user contextual attributes (eg, location, time, history …

Crowdfunding is an emerging internet platform that provides financial support for people in
need. With the development of crowdfunding platforms, the number of projects released on …

We present Distributed Equivalent Substitution (DES) training, a novel distributed training
framework for large-scale recommender systems with dynamic sparse features. DES …

Crafting the right keywords and crafting their ad creatives is an arduous task that requires
the collaboration of online marketers, creative directors, data scientists, and possibly …