🔮Trying to find the best movie to watch on Netflix can be a daunting. Case Study for Recommendation System of movies in Netflix.🔧

This repository contains collaborative filtering recommender system build in Python with surprise package to predict book ratings in Book-Crossing dataset.

This repo contains my practice and template code for all kinds of recommender systems using SupriseLib. More complex and hybrid Recommender Systems can build on top of these template codes.

Created Recommender systems using TMDB movie dataset by leveraging the concepts of Content Based Systems and Collaborative Filtering.

Deployed Product Recommendation Model using collaborative filtering.

在Yelp数据集上摘取部分评分数据进行多种推荐算法（SVD,SVDPP,PMF,NMF）的性能对比。Some rating data are extracted from yelp dataset to compare the performance of various recommendation algorithms(SVD,SVDPP,PMF,NMF).

使用矩阵分解方法进行电影推荐的评分预测。The matrix factorization method is used to predict the rating of movie recommendation.

Simple Recommender Systems

This repository covers a project of creating a recommendation system using collaborative filtering on the Grouplens movielens database. The surprise library is utilized to test out different models (KNN Basic, KNN Baseline, and SVD). SVD was found to be the most accurate and then was implemented into the system. The cold start problem was addres…

A case study of the Netflix Prize solution where, given anonymous data of users and the ratings given to movies, the objective to provide recommendations to users for movies which they would like, based on their past activity and taste.

Implementation for two different types of recommendation systems (Content-based and collaborative filtering)

Predicted missing ratings using SVD algorithm from the Surprise Library for items from a file containing user ratings for multiple items by comparing a user’s ratings for available items with those of other user’s ratings and the project was built in Python

Movie recommendation system to find common movie interests among a group of people.

Using a dataset from MovieLens, a movie recommendation system was created that recommends to users which movies they will like. The system also goes a step further to solve the cold start problem, which is when there is a new user in the dataset and there is no prior information on them. This system also finds a solution to this.

Using the MovieLens dataset with Surprise to compare different algorithms for rating prediction, and also create a movie recommendation system on top of it.

Movie recommendation system with Collaborative filtering and kNN recommendation, featuring streamlit frontend

Système de recommandation

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