LAPACK development repository

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

A Comparative Framework for Multimodal Recommender Systems

MADS: Model Analysis & Decision Support

Non-Negative Matrix Tri-Factorization for Co-clustering

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！

This website applies a recommendation system and continuous learning.

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

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

Block Linear Algebra Algorithms in Matlab

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.

Fast Clojure Matrix Library

math, linear algebra, matrix and other helpers

A Comprehensive Framework for Building End-to-End Recommendation Systems with State-of-the-Art Models

Competition for the Recommender Systems course @ PoliMi. The objective is to recommend relevant TV shows to users. Models were evaluated on their MAP@10.

Matrix toolbox

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.

Geostatistical Inversion

pytorch version of neural collaborative filtering