Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
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Updated
Apr 18, 2023 - R
Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)
Computational Methods for Numerical Analysis
Factoried Personalized Markov Chains for Next Basket Recommendation in R and Python
Matrix factorization-based biological discovery from large-scale transcriptome data using easyMF
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation in R and Python
R Package: Regularized Principal Component Analysis for Spatial Data
R package implementing Bayesian NMF using various models and prior structures.
Low Rank Matrix Factorization S3 Objects
Code to reproduce Adaptive elastic-net sparse PCA for robust cross-species, cross-platform analysis of complex gene expression data in Alzheimer’s disease (Hin et al.)
R interface to the fastFM library
mfair: Matrix Factorization with Auxiliary Information in R
Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。
This contains implementation of eigenvalue calculation algorithms from scratch.
R Package: Regularized Principal Component Analysis for Spatial Data
Probabilistic Matrix Factorization for Recommendation by R。使用R语言实现了矩阵分解、概率矩阵分解算法。
Product Recommender Engine - Use Case: 'The MovieLens 10M dataset'
Interpretive Structural Modelling (ISM). Returns a minimum-edge hierarchical digraph following J.N. Warfield's graph partitioning algorithm.
The main task of a recommender system is to predict the users responce to different options. This is my solution for the first capstone project in the course 'Professional Certificate in Data Science' provided by Harvard University (HarvardX) on EDX.
Probabilistic Matrix Factorization with JAGS in R
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