# DLRM - [Deep Learning Recommendation Model for Personalization and Recommendation Systems](https://arxiv.org/abs/1906.00091) - DLRM - The DLRM model handles continuous (dense) and categorical (sparse) [Features](Features.md) 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](Parallelization.md) scheme utilizing model parallelism on the [Embedding](Embedding.md) tables to mitigate memory constraints while exploiting data parallelism to scale-out compute from the fully-connected [Layers](Layers.md) - it computes the feature interactions explicitly while limiting the order of interaction to pairwise interactions. - treats each embedded feature vector (corresponding to categorical [Features](Features.md)) 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