zero-dependency image processing library

Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.

Differentiable matrix factorizations using ImplicitDifferentiation.jl.

Recommend movies to users by RBMs, TruncatedSVD, Stochastic SVD and Variational Inference

Includes the SVD-based approximation algorithms for compressing deep learning models and the FPGA accelerators exploiting such approximation mechanism, as described in the paper Mapping multiple LSTM models on FPGAs.

Projet d'étude système de recommendation en utilisant filtrage collaboratif

Code of Book Recommendation System, suggests books to users based on their interests and reading history along with the content of the item.

CVM Class Library

Применяем Singular Value Decomposition with implicit feedback для задачи рекомендации аниме

Hybrid RecSys, CF-based RecSys, Model-based RecSys, Content-based RecSys, Finding similar items using Jaccard similarity

redsvd - RandomizED Singular Value Decomposition

This project demonstrates the application of Singular Value Decomposition (SVD) for image compression using Python and NumPy.

Coursework for CS550 : Massive Data Mining. Topics covered include Map-Reduce, Association Rules, Frequent Itemsets, Locality-Sensitive Hashing (LSH), Singular Value Decomposition (SVD), Page Rank, k-means, Modularity, Spectral Clustering, Clique-based communities, Clustering Data Streams.

Building a Collaborative Filtering based Recommender system using e-commerce data.

Sparse Truncated SVD Benchmark (Python)

Movie recommendation system based on popularity and also using KNN and Cosine similarity. 🎥🍿

In this project we are comparing two approaches for movie recommendation for a new user or existing user based on their age, gender, occupation.

GPGPU-based SVD solver for large dense matrices.

CSE523 Machine Learning SSSR repository contains a movie recommendation system using KNN, ALS, and SVD algorithm.

I want to crawl imdb movie data and want to recommend movies based on various features of individual movies