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
- Recommender system (추천 시스템)
- Collaborative filtering (협업 필터링)
- Content-based filtering (컨텐츠 기반 필터링)
- Hybrid recommender systems