Build a movies recommendation system clone using Movielens dataset to construct recommendation system such as Simple recommender, Content based recommender (based on movie description and metadata) , Collaborative-Filtering based recommender , and a Hybrid recommender system.

Evaluating and comparing recommender system models using MovieLens-Ratings dataset

To recommend the next 10 movies to the user using the Prized Dataset provided by Netflix - over the span of 10 days for Capstone Project.

A book recommendation system using model based collabritive filtering. It is based on SVD machine learning model. It generate top 10 recommendation of books.Here i used surprise library.

영화 추천 시스템

Simple Recommender Systems

Système de recommandation

Predict user rating for a netflix movie.

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.

Did you ever wonder how the recommendations on Netflix work? Find out in this project, where I build three basic movie recommenders and implement them in a streamlit App.

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

A Book Recommender System: Collaborative Filtering using Surprise (k-NN Baseline model)

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.

Elice Generative AI Edu Hackaton TEAM10 <AI오늘>

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

I created a recommender system using a Python scikit named Surprise. The purpose of building this system is to predict a person's preferences so the user can find what they are looking for faster.

The project used Python to create a personalized book recommendation system that analyzed users' past ratings on books to identify their preferences and patterns and suggested books that the user is likely to enjoy but has not read yet.

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