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Starreco

State-of-The-Art Rating-based RECOmmendation system: pytorch lightning implementation

starreco is a Pytorch lightning implementation for a series of SOTA deep learning rating-based recommendation systems. This repository also serves as a part of the author's master thesis work's literature review.

Features

  • Up to 20+ recommendation models across 20 publications.
  • Built on top of Pytorch lightning.
  • GPU acceleration execution.
  • Reducing memory usage for large sparse matrices.
  • Simple and understandable code.
  • Easy extension and code reusability.

Research Models

  • MF - Matrix Factorization
  • GMF - Generalized Matrix Factorization
  • MLP - Multilayer Perceptrons
  • NeuMF - Neural Matrix Factorization
  • FM - Factorization Machine
  • NeuFM - Neural Factorization Machine
  • WDL - Wide & Deep Learning
  • DeepFM - Deep Factorization Machine
  • xDeepFM - Extreme Deep Factorization Machine
  • FGCNN - Feature Generation by using Convolutional Neural Network
  • ONCF - Outer-based Product Neural Collaborative Filtering
  • CNNDCF - Convolutional Neural Network based Deep Colloborative Filtering
  • ConvMF - Convolutional Matrix Factorization
  • AutoRec - AutoRec
  • DeepRec - DeepRec
  • CFN - Collaborative Filtering Network
  • CDAE - Collaborative Denoising AutoEncoder
  • CCAE - Collaborative Convolutional AutoEncoder
  • SDAECF - Stacked Denoising AutoEncoder for Collaborative Filtering
  • mDACF - marginalized Denoising AutoEncoder Collaborative Filtering
  • GMF++ - Generalized Matrix Factorization ++
  • MLP++ - Multilayer Perceptrons ++
  • NeuMF++ - Neural Matrix Factorization ++

See also

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