mlsanityData to 14 Oct 2026

Deep Learning Recommendation Model for Personalization and Recommendation Systems

arXiv 1906.00091 ↗First posted 31 May 2019cs.IR cs.LGcited by 327

Not on the map. Why?

Abstract (arXiv)

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. We compare DLRM against existing recommendation models and characterize its performance on the Big Basin AI platform, demonstrating its usefulness as a benchmark for future algorithmic experimentation and system co-design.

Key facts. Cited by 327 papers in the map (data through 14 Oct 2026). First posted on arXiv 31 May 2019.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2020: 1; 2021: 51; 2022: 55; 2023: 70; 2024: 63; 2025: 45; 2026: 42.

327 papers.

Sort:

By area:

  1. 2021A Survey of Quantization Methods for Efficient Neural Network Inferencecited by 815
  2. 2023LaMP: When Large Language Models Meet Personalizationcited by 437
  3. 2021Sustainable AI: Environmental Implications, Challenges and Opportunitiescited by 242
  4. 2024Large Language Models for Education: A Survey and Outlookcited by 188
  5. 2022Compute Trends Across Three Eras of Machine Learningcited by 163
  6. 2024The Road Less Scheduledcited by 163
  7. 2023Microscaling Data Formats for Deep Learningcited by 152
  8. 2023Prodigy: An Expeditiously Adaptive Parameter-Free Learnercited by 133
  9. 2023Learning-Rate-Free Learning by D-Adaptationcited by 113
  10. 2024Optimization Methods for Personalizing Large Language Models through Retrieval Augmentationcited by 99
  11. 2024AI and Memory Wallcited by 92
  12. 2023Data Distillation: A Surveycited by 87
38 more
  1. 2021Software-Hardware Co-design for Fast and Scalable Training of Deep Learning Recommendation Modelscited by 78
  2. 2023A Distributed Data-Parallel PyTorch Implementation of the Distributed Shampoo Optimizer for Training Neural Networks At-Scalecited by 77
  3. 2021Pixelated Butterfly: Simple and Efficient Sparse training for Neural Network Modelscited by 76
  4. 2024Wukong: Towards a Scaling Law for Large-Scale Recommendationcited by 74
  5. 2021TT-Rec: Tensor Train Compression for Deep Learning Recommendation Modelscited by 58
  6. 2023With Shared Microexponents, A Little Shifting Goes a Long Waycited by 50
  7. 2023Optimal Linear Decay Learning Rate Schedules and Further Refinementscited by 43
  8. 2021Torch.fx: Practical Program Capture and Transformation for Deep Learning in Pythoncited by 40
  9. 2023Oobleck: Resilient Distributed Training of Large Models Using Pipeline Templatescited by 40
  10. 2022Machine Learning Model Sizes and the Parameter Gapcited by 39
  11. 2022Understanding Scaling Laws for Recommendation Modelscited by 38
  12. 2022BlindFL: Vertical Federated Machine Learning without Peeking into Your Datacited by 36
  13. 2021RecSSD: Near Data Processing for Solid State Drive Based Recommendation Inferencecited by 35
  14. 2022MetaBalance: Improving Multi-Task Recommendations via Adapting Gradient Magnitudes of Auxiliary Taskscited by 34
  15. 2025LaMP-QA: A Benchmark for Personalized Long-form Question Answeringcited by 31
  16. 2022Tensor Program Optimization with Probabilistic Programscited by 30
  17. 2023Efficient Post-training Quantization with FP8 Formatscited by 29
  18. 2021Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Trainingcited by 28
  19. 2021HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Frameworkcited by 28
  20. 2022RecShard: Statistical Feature-Based Memory Optimization for Industry-Scale Neural Recommendationcited by 28
  21. 2024Exploring Safety-Utility Trade-Offs in Personalized Language Modelscited by 27
  22. 2025Reasoning-Enhanced Self-Training for Long-Form Personalized Text Generationcited by 27
  23. 2025A Survey of Personalization: From RAG to Agentcited by 27
  24. 2023CASSINI: Network-Aware Job Scheduling in Machine Learning Clusterscited by 26
  25. 2025ExPerT: Effective and Explainable Evaluation of Personalized Long-Form Text Generationcited by 26
  26. 2021Large Graph Convolutional Network Training with GPU-Oriented Data Communication Architecturecited by 25
  27. 2021Accelerating Recommendation System Training by Leveraging Popular Choicescited by 22
  28. 2021CoSA: Scheduling by Constrained Optimization for Spatial Acceleratorscited by 22
  29. 2024MMREC: LLM Based Multi-Modal Recommender Systemcited by 22
  30. 2022SurCo: Learning Linear Surrogates For Combinatorial Nonlinear Optimization Problemscited by 21
  31. 2023Scaling User Modeling: Large-scale Online User Representations for Ads Personalization in Metacited by 21
  32. 2023Model Extraction Attacks Revisitedcited by 21
  33. 2022Infinite Recommendation Networks: A Data-Centric Approachcited by 20
  34. 2022Merlin HugeCTR: GPU-accelerated Recommender System Training and Inferencecited by 20
  35. 2023Cross-domain recommendation via user interest alignmentcited by 19
  36. 2025Enhancing Embedding Representation Stability in Recommendation Systems with Semantic IDcited by 19
  37. 2021Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performancecited by 18
  38. 2025OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking Systemcited by 18

50 most cited of 327 All 327 (JSON)