R interface to the fastFM library
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Updated
Jun 29, 2017 - R
R interface to the fastFM library
Probabilistic Matrix Factorization with JAGS in R
Fusing Similarity Models with Markov Chains for Sparse Sequential Recommendation in R and Python
Factoried Personalized Markov Chains for Next Basket Recommendation in R and Python
🍱 R implementation for selected machine learning methods with deep learning frameworks (Keras, Tensorflow)
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 for Recommendation by R。使用R语言实现了矩阵分解、概率矩阵分解算法。
Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。
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.
Low Rank Matrix Factorization S3 Objects
The purpose of the present project is to create a recommendation system for predicting the rating of movies.
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.)
Matrix factorization-based biological discovery from large-scale transcriptome data using easyMF
This contains implementation of eigenvalue calculation algorithms from scratch.
Fast and accurate machine learning on sparse matrices - matrix factorizations, regression, classification, top-N recommendations.
Computational Methods for Numerical Analysis
mfair: Matrix Factorization with Auxiliary Information in R
R Package: Regularized Principal Component Analysis for Spatial Data
R Package: Regularized Principal Component Analysis for Spatial Data
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