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 …

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 …

As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …

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 …

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

Click Through Rate (CTR) prediction plays an essential role in recommender systems and
online advertising. It is crucial to effectively model feature interactions to improve the …

The travel marketing platform of Alibaba serves an indispensable role for hundreds of
different travel scenarios from Fliggy, Taobao, Alipay apps, etc. To provide personalized …

In spite of the tremendous development of recommender system owing to the progressive
capability of machine learning recently, the current recommender system is still vulnerable to …

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