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 …

Recommender system plays a vital role in various online services. However, its insulated
nature of training and deploying separately within a specific closed domain limits its access …

With the rapid development of online services and web applications, recommender systems
(RS) have become increasingly indispensable for mitigating information overload and …

Graph-structured data are pervasive in the real-world such as social networks, molecular
graphs and transaction networks. Graph neural networks (GNNs) have achieved great …

Click-through rate (CTR) prediction is one of the fundamental tasks in online advertising and
recommendation. Multi-layer perceptron (MLP) serves as a core component in many deep …

The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …

With the widespread application of online advertising systems, click-through rate (CTR)
prediction has received more and more attention and research. The most prominent features …

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-
standing challenge for Graph Neural Networks (GNNs) due to the overwhelming …

Many Click-Through Rate (CTR) prediction works focused on designing advanced
architectures to model complex feature interactions but neglected the importance of feature …

We present Tencent's ads recommendation system and examine the challenges and
practices of learning appropriate recommendation representations. Our study begins by …