Deep Learning Recommendation Model for Personalization and Recommendation Systems

Understand

With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.

• These networks differ significantly from other deep learning networks due to their need to handle categorical features and are not well studied or understood.
• In this paper, we develop a state-of-the-art deep learning recommendation model (DLRM) and provide its implementation in both PyTorch and Caffe2 frameworks.
• In addition, we design a specialized parallelization scheme utilizing model parallelism on the embedding tables to mitigate memory constraints while exploiting data parallelism to scale-out compute from the fully-connected layers.