A comprehensive survey on trustworthy recommender systems
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 …
people make appropriate decisions in an effective and efficient way, by providing …
Consumer credit risk assessment: A review from the state-of-the-art classification algorithms, data traits, and learning methods
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 …
research on consumer credit risk assessment in recent decades, the abundance of literature …
One model to serve all: Star topology adaptive recommender for multi-domain ctr prediction
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 …
models and then serve the domain. However, a large-scale commercial platform often …
Software-hardware co-design for fast and scalable training of deep learning recommendation models
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 …
critical services at Meta and are the single largest AI application in terms of infrastructure …
RecShard: statistical feature-based memory optimization for industry-scale neural recommendation
We propose RecShard, a fine-grained embedding table (EMB) partitioning and placement
technique for deep learning recommendation models (DLRMs). RecShard is designed …
technique for deep learning recommendation models (DLRMs). RecShard is designed …
Monolith: real time recommendation system with collisionless embedding table
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 …
driven by time-sensitive customer feedback, such as short-videos ranking or online ads …
Ekko: A {Large-Scale} deep learning recommender system with {Low-Latency} model update
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 …
promptly serving new users and content. Existing DLRSs, however, fail to do so. They …
Deep learning training in facebook data centers: Design of scale-up and scale-out systems
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 …
learning models. At Facebook we use many different models, including computer vision …
Heterps: Distributed deep learning with reinforcement learning based scheduling in heterogeneous environments
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 …
achieve excellent performance. The training process of DNN models generally handles …
PICASSO: Unleashing the Potential of GPU-centric Training for Wide-and-deep Recommender Systems
The development of personalized recommendation has significantly improved the accuracy
of information matching and the revenue of e-commerce platforms. Recently, it has two …
of information matching and the revenue of e-commerce platforms. Recently, it has two …