Projet d'étude système de recommendation en utilisant filtrage collaboratif

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

The goal of this project was to build an explicit recommender system using collaborative filtering for restaurants in Charlotte using Yelp's Open Dataset. I wanted to explore the mechanics of recommendations systems, and explore a new library in Surprise.

Implementation and comparison (time, space) of SVD and CUR matrix decomposition algorithms

Implementation of various recommendation algorithms such as Collaborative filtering, SVD and CUR-decomposition to predict user movie ratings

Recommendation system using collaborative filtering on a movie dataset

The Genius You is a growing platform where the user has options to set daily achieving targets and goals as per the duration locked to accomplish his scope for improvement. This platform provides to improves his\her daily targets such as reading, studying, reducing phone usage, etc. This code is developed to Analysis of the goals that are being …

Movie Recommendation System created using Singular Value Decomposition (SVD).

Anime Recommendation System based on Item-CF and SVD algorithms

A comprehensive full-stack solution that harnesses AI and integrates multiple APIs to streamline and enhance the eco-tourism experience.

🐝 Materials and homework assignments for HSE recommender systems course

Recommendation engine(Collaborative filtering)

Recomender System for Yandex.Cup22

Web app that uses web scraping to give film recommendations using an SVD collaborative filtering model for any Letterboxd user or a recommendation for two using Blend mode. Can include filters such as film popularity, film genre, and films in user's watchlist.

In this task, a decision was made to build 3 models of recommendation systems based on the MovieLens Small dataset.