About Machine Learning Model DLRM

Deep Learning Recommendation Model (DLRM) is a neural network-based model designed for personalization and recommendation systems. Developed by a team from Facebook, DLRM was introduced to address the unique challenges of handling categorical features in recommendation models. Unlike traditional deep learning networks, DLRM processes categorical data through embeddings and dense features using a multilayer perceptron (MLP). This design is instrumental in tasks like ad click-through rate prediction and rankings in large internet companies. The model was introduced with an open-source implementation in both PyTorch and Caffe2 frameworks and includes a specialized parallelization scheme for optimizing memory usage and computational efficiency.

Model Card for DLRM

• Model Details
  • Developing Organization: Facebook
  • Model Date: Not explicitly mentioned in the primary source.
  • Model Version: Not specified in the primary source.
  • Model Type: Neural network-based recommendation model.
  • Training Algorithms and Features: Uses embeddings for categorical data and MLP for dense features.
  • More Information: DLRM on ar5iv
  • Citation: [1906.00091] Deep Learning Recommendation Model for Personalization and Recommendation Systems.
  • License: Open-source (specific license not mentioned).
• Intended Use
  • Primary Uses: Personalization and recommendation in large internet companies, especially in ad click-through rate prediction and rankings.
  • Primary Users: Developers and researchers in recommendation systems.
  • Out-of-scope Uses: Not specified.
• Factors
  • Relevant Factors: Effective in handling categorical features.
  • Evaluation Factors: [More Information Needed]
• Metrics
  • Performance Measures: Focuses on memory efficiency and computational scalability.
  • Decision Thresholds: [More Information Needed]
  • Variation Approaches: [More Information Needed]
• Evaluation Data
  • Datasets: [More Information Needed]
  • Motivation: [More Information Needed]
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Preprocessing:** [More Information Needed]

• Training Data
  • [More Information Needed]
• Quantitative Analyses
  • [More Information Needed]
• Ethical Considerations
  • [More Information Needed]
• Caveats and Recommendations
  • [More Information Needed]

References

1. arXiv - Deep Learning Recommendation Model
2. Meta AI - Deep Learning Recommendation Model