While recent years have witnessed a rapid growth of research papers on recommender
system (RS), most of the papers focus on inventing machine learning models to better fit …

Accurate estimation of post-click conversion rate is critical for building recommender
systems, which has long been confronted with sample selection bias and data sparsity …

Post-click conversion, as a strong signal indicating the user preference, is salutary for
building recommender systems. However, accurately estimating the post-click conversion …

Post-click conversion rate (CVR) estimation is a fundamental task in developing effective
recommender systems, yet it faces challenges from data sparsity and sample selection bias …

Conversion Rate (CVR) prediction in modern industrial e-commerce platforms is becoming
increasingly important, which directly contributes to the final revenue. In order to address the …

In our daily life, we are often greatly assisted with recommendation engines in finding the
required information efficiently and accurately. In this paper, we focus on an emerging and …

Conversion rate prediction is critical to many online applications such as digital display
advertising. To capture dynamic data distribution, industrial systems often require retraining …

Recent work in recommender systems has emphasized the importance of fairness, with a
particular interest in bias and transparency, in addition to predictive accuracy. In this paper …

In recommendation scenarios, there are two long-standing challenges, ie, selection bias and
data sparsity, which lead to a significant drop in prediction accuracy for both Click-Through …

Real-Time Bidding (RTB) is one of the most important forms of online advertising, where an
auction is hosted in real time to sell the individual ad impression. How to design an …