The advent of large language models marks a revolutionary breakthrough in artificial
intelligence. With the unprecedented scale of training and model parameters, the capability …

Recommender systems play a significant role in information filtering and have been utilized
in different scenarios, such as e-commerce and social media. With the prosperity of deep …

Click-through rate (CTR) prediction plays a critical role in recommender systems and online
advertising. The data used in these applications are multi-field categorical data, where each …

Efficient collective communication is crucial to parallel-computing applications such as
distributed training of large-scale recommendation systems and natural language …

Recommender systems have been widely deployed in various real-world applications to
help users identify content of interest from massive amounts of information. Traditional …

Graph neural networks (GNNs) are currently one of the most performant and versatile
collaborative filtering methods. Meanwhile, like in traditional collaborative filtering, owing to …

The advent of switches with programmable dataplanes has enabled the rapid development
of new network functionality, as well as providing a platform for acceleration of a broad …

Click-through rate (CTR) prediction model usually consists of three components: embedding
table, feature interaction layer, and classifier. Learning embedding table plays a …

Recent years have witnessed the rapid development of online micro-video platforms, in
which the recommender system plays an essential role in overcoming the information …

As a crucial component of most modern deep recommender systems, feature embedding
maps high-dimensional sparse user/item features into low-dimensional dense embeddings …