Feature selection plays an impactful role in deep recommender systems, which selects a
subset of the most predictive features, so as to boost the recommendation performance and …

Designing an effective loss function plays a crucial role in training deep recommender
systems. Most existing works often leverage a predefined and fixed loss function that could …

Deep neural network (DNN) models have been widely used for click-through rate (CTR)
prediction in online advertising. The training framework typically consists of embedding …

Factorization machines are a generic supervised method for a wide range of tasks in the
field of artificial intelligence, such as prediction, inference, etc., which can effectively model …

In the advertising and marketing of e-commerce platform, click rate prediction is directly
related to the revenue of e-commerce platform. In this paper, we propose an advertising click …

Deep recommender systems (DRS) are critical for current commercial online service
providers, which address the issue of information overload by recommending items that are …

Deep sparse networks (DSNs), of which the crux is exploring the high-order feature
interactions, have become the state-of-the-art on the prediction task with high-sparsity …

Click-through rate (CTR) prediction aims to recall the advertisements that users are
interested in and to lead users to click, which is of critical importance for a variety of online …

CTR (Click-Through Rate) prediction has attracted more and more attention from academia
and industry for its significant contribution to revenue. In the last decade, learning feature …

Dr. Xiangyu Zhao is an assistant professor of the school of data science at City University of
Hong Kong (CityU). Prior to CityU, he completed his PhD (2021) at MSU under the advisory …