facebookresearch/dlrm

Health v2 · maintenance only

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Adoption not part of the score

4065 stars · 860 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch-based implementation of the Deep Learning Recommendation Model (DLRM) from Facebook Research, which processes dense and sparse features through MLPs, embedding tables, and feature interaction operators to predict click-through probability. It serves as both a reference implementation of the DLRM architecture and a benchmark for recommendation system hardware and software.

Use cases

• implement a deep learning recommendation model
• train a click-through rate prediction model
• benchmark recommendation model performance on GPUs
• learn how sparse features and embedding tables work in recommenders
• reproduce the DLRM paper results
• prototype personalized recommendation systems

When to choose

• you need a reference implementation of the DLRM architecture
• you're benchmarking recommendation workloads on GPUs or CPUs
• you want to experiment with dense/sparse feature interaction models
• you're doing research on recommendation systems

When to avoid

• you need a production-ready, full-featured recommendation platform
• you want a non-PyTorch framework like TensorFlow
• you need collaborative filtering out of the box rather than a neural model
• you're looking for a hosted recommendation service

Facets

library · maturity stable

machine-learning deep-learning benchmarking machine-learning deep-learning python cross-platform recommendation-model dlrm pytorch ctr-prediction embedding-tables facebook-research recommendation-systems gpu linux

Member repositories

For agents

markdown · JSON · MCP: product_card(name="facebookresearch/dlrm")