Modularized Fortran LAPACK implementation

Non-Negative Matrix Tri-Factorization for Co-clustering

R package implementing Bayesian NMF using various models and prior structures.

LAPACK development repository

This study aims to investigate the effectiveness of three Transformers (BERT, RoBERTa, XLNet) in handling data sparsity and cold start problems in the recommender system. We present a Transformer-based hybrid recommender system that predicts missing ratings and ex- tracts semantic embeddings from user reviews to mitigate the issues.

Movie Recommendation System created using Collaborative Filtering (Website) and Content based Filtering (Jupyter Notebook)

math, linear algebra, matrix and other helpers

Block Linear Algebra Algorithms in Matlab

This website applies a recommendation system and continuous learning.

R Package: Regularized Principal Component Analysis for Spatial Data

A Comparative Framework for Multimodal Recommender Systems

A Python 3 toolbox for neural receptive field estimation using splines and Gaussian priors.

A library for butterfly and hierarchical matrix factorizations.

A recommender system built from scratch using the collaboration filtering algorithm and NumPy library

MADS: Model Analysis & Decision Support

A Python implementation of LightFM, a hybrid recommendation algorithm.

Ultra-Fast Principal Component Analysis All in One

Book_4_《矩阵力量》 | 鸢尾花书：从加减乘除到机器学习；上架！

Recommender System project that uses Weighted Matrix Factorisation to learn user and items embeddings from a (sparse) feedbacks matrix, and uses them to perform user-specific suggestions

The main aim of the project is to develop a web-based application that is going to make it possible for the customer to place an order of food by using this app . In this we are also creating food recommendation app and that will substitute the manual system of the placing an order with an automated one.