Recommender Systems (RS) play an integral role in enhancing user experiences by
providing personalized item suggestions. This survey reviews the progress in RS inclusively …

Multi-layer perceptron (MLP) serves as a core component in many deep models for click-
through rate (CTR) prediction. However, vanilla MLP networks are inefficient in learning …

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

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 …

Click-through rate (CTR) prediction is a crucial area of research in online advertising. While
binary cross entropy (BCE) has been widely used as the optimization objective for treating …

Traditional approaches for learning on categorical data underexploit the dependencies
between columns (aka fields) in a dataset because they rely on the embedding of data …

Recent advances in deep foundation models have led to a promising trend of developing
large recommendation models to leverage vast amounts of available data. However, we …

The goal of click-through rate (CTR) prediction in recommender systems is to effectively
work with input features. However, existing CTR prediction models face three main issues …

Click-through rate (CTR) prediction is an essential component of industrial multimedia
recommendation, and the key to enhancing the accuracy of CTR prediction lies in the …

With the applications of recommendation systems rapidly expanding, an increasing number
of studies have focused on every aspect of recommender systems with different data inputs …