DLRM

• Deep Learning Recommendation Model for Personalization and Recommendation Systems
• DLRM
• The DLRM model handles continuous (dense) and categorical (sparse) Features that describe users and products
• wide range of hardware and system components, such as memory capacity and bandwidth, as well as communication and compute resources
• 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
• it computes the feature interactions explicitly while limiting the order of interaction to pairwise interactions.
• treats each embedded feature vector (corresponding to categorical Features) as a single unit, whereas other methods (such as Deep and Cross) treat each element in the feature vector as a new unit that should yield different cross terms
• These design choices help reduce computational/memory cost while maintaining competitive accuracy