Credit risk assessment is a crucial element in credit risk management. With the extensive
research on consumer credit risk assessment in recent decades, the abundance of literature …

As one of the most successful AI-powered applications, recommender systems aim to help
people make appropriate decisions in an effective and efficient way, by providing …

Deep learning recommendation models (DLRMs) have been used across many business-
critical services at Meta and are the single largest AI application in terms of infrastructure …

Traditional industry recommendation systems usually use data in a single domain to train
models and then serve the domain. However, a large-scale commercial platform often …

We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement
technique for deep learning recommendation models (DLRMs). RecShard is designed …

Building a scalable and real-time recommendation system is vital for many businesses
driven by time-sensitive customer feedback, such as short-videos ranking or online ads …

Large-scale training is important to ensure high performance and accuracy of machine-
learning models. At Facebook we use many different models, including computer vision …

Deep Learning Recommender Systems (DLRSs) need to update models at low latency, thus
promptly serving new users and content. Existing DLRSs, however, fail to do so. They …

Recommender models are commonly used to suggest relevant items to a user for e-
commerce and online advertisement-based applications. These models use massive …

Deep neural networks (DNNs) exploit many layers and a large number of parameters to
achieve excellent performance. The training process of DNN models generally handles …