Modularized Fortran LAPACK implementation
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Updated
Jun 2, 2024 - Fortran
Modularized Fortran LAPACK implementation
Non-Negative Matrix Tri-Factorization for Co-clustering
R package implementing Bayesian NMF using various models and prior structures.
A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models
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
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