Recommender System
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Updated
Sep 5, 2017 - Python
Recommender System
A prior learning and sampling model informed tool for learning with Single Cell RNA-Seq data
Autoencoding Topographic Factors
Articles recommendation engine for IBM Watson Studio platform
Matrix ADT for linear algebra applications.
Multi-source propagation aware network clustering (MSPANC) published in Neurocomputing 2021
Ressources usage optimisation, memory and calculation... using Cuthill Mac-Kee algorithm. Afterward, optimised matrix is solved with LDLT factorization.
I built recommender systems for recommending products to user using Model-based recommendation system.
Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices.
This repository contains the code for a book recommendation system that uses natural language processing techniques to recommend books to users based on their preferences.
Text summarization based on SVD & NMF
Personalized movie recommendation research in MovieLens dataset. The current model can predict movie ratings with RMSE 0.7672.
Finds the L factor for a given mxn matrix.
matrix wrapper for Fortran 95
The repository has the codes to identify co-linear (linearly dependent columns in a data) using linear algebra techniques.
Implementation of recommender systems using collaborative filtering (with and without baseline), SVD, and CUR.
Using Matrix Factorization/Probabilistic Matrix Factorization to solve Recommendation。矩阵分解进行推荐系统算法。
Perform Sparse Matrix Factorization using GPU in CUDA
A simple matrix library written in Rust, with LU decomposition, equation solving, inversion and more.
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