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An implementation of a deep learning recommendation model (DLRM)
| Date | Stars |
|---|---|
| 2026-07-31 | 4060 |
| 2026-08-01 | 4060 |
| 2026-08-03 | 4060 |
| 2026-08-06 | 4062 |
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Deep Learning Recommendation Model for Personalization and Recommendation Systems:
=================================================================================
*Copyright (c) Facebook, Inc. and its affiliates.*
Description:
------------
An implementation of a deep learning recommendation model (DLRM).
The model input consists of dense and sparse features. The former is a vector
of floating point values. The latter is a list of sparse indices into
embedding tables, which consist of vectors of floating point values.
The selected vectors are passed to mlp networks denoted by triangles,
in some cases the vectors are interacted through operators (Ops).
```
output:
probability of a click
model: |
/\
/__\
|
_____________________> Op <___________________
/ | \
/\ /\ /\
/__\ /__\ ... /__\
| | |
| Op Op
| ____/__\_____ ____/__\____
| |_Emb_|____|__| ... |_Emb_|__|___|
input:
[ dense features ] [sparse indices] , ..., [sparse indices]
```
More precise definition of model layers:
1) fully connected layers of an mlp
z = f(y)
y = Wx + b
2) embedding lookup (for a list of sparse indices p=[p1,...,pk])
z = Op(e1,...,ek)
obtain vectors e1=E[:,p1], ..., ek=E[:,pk]
3) Operator Op can be one of the following
Sum(e1,...,ek) = e1 + ... + ek
Dot(e1,...,ek) = [e1'e1, ..., e1'ek, ..., ek'e1, ..., ek'ek]
Cat(e1,...,ek) = [e1', ..., ek']'
where ' denotes transpose operation
See our blog post to learn more about DLRM: [https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/](https://ai.facebook.com/blog/dlrm-an-advanced-open-source-deep-learning-recommendation-model/).
Cite [Work](https://arxiv.org/abs/1906.00091):
```
@article{DLRM19,
author = {Maxim Naumov and Dheevatsa Mudigere and Hao{-}Jun Michael Shi and Jianyu Huang and Narayanan Sundaraman and Jongsoo Park and Xiaodong Wang and Udit Gupta and Carole{-}Jean Wu and Alisson G. Azzolini and Dmytro Dzhulgakov and Andrey Mallevich and Ilia Cherniavskii and Yinghai Lu and Raghuraman Krishnamoorthi and Ansha Yu and Volodymyr Kondratenko and Stephanie Pereira and Xianjie Chen and Wenlin Chen and Vijay Rao and Bill Jia and Liang Xiong and Misha Smelyanskiy},
title = {Deep Learning Recommendation Model for Personalization and Recommendation Systems},
journal = {CoRR},
volume = {abs/1906.00091},
year = {2019},
url = {https://arxiv.org/abs/1906.00091},
}
```
Related Work:
On the [system architecture implications](https://arxiv.org/abs/1906.03109), with DLRM as one of the benchmarks,
```
@article{ArchImpl19,
author = {Udit Gupta and Xiaodong Wang and Maxim Naumov and Carole{-}Jean Wu and Brandon Reagen and David Brooks and Bradford Cottel and Kim M. Hazelwood and Bill Jia and Hsien{-}Hsin S. Lee and Andrey Malevich and Dheevatsa Mudigere and Mikhail Smelyanskiy and Liang Xiong and Xuan Zhang},
title = {The Architectural Implications of Facebook's DNN-based Personalized Recommendation},
journal = {CoRR},
volume = {abs/1906.03109},
year = {2019},
url = {https://arxiv.org/abs/1906.03109},
}
```
On the [embedding compression techniques (for number of vectors)](https://arxiv.org/abs/1909.02107), with DLRM as one of the benchmarks,
```
@article{QuoRemTrick19,
author = {Hao{-}Jun Michael Shi and Dheevatsa Mudigere and Maxim Naumov and Jiyan Yang},
title = {Compositional Embeddings Using Complementary Partitions for Memory-Efficient Recommendation Systems},
journal = {CoRR},
volume = {abs/1909.02107},
year =Excerpt of 15,524 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:725e037f17f36788, llm:description: An implementation of a deep learning recommendation model (DLRM) (facebookresearch/dlrm)
matched fp:725e037f17f36788, llm:description: An implementation of a deep learning recommendation model (DLRM) (facebookresearch/dlrm)
matched fp:725e037f17f36788, llm:description: An implementation of a deep learning recommendation model (DLRM) (facebookresearch/dlrm)
