Matrix factorization using alternating least squares for movie recommendation
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Updated
Apr 17, 2020 - Julia
Matrix factorization using alternating least squares for movie recommendation
Mediatrac is a web app that allows users to check out information on new and upcoming movies as well as build a library of their favorite classic films.
A simple Movie Recommendation Software using Python
A Movie recommender system that reads overviews of movies and generates TF-IDF matrix and finds cosine similarity of each movie with other movies and displays the similar movies
This project involves developing a content-based recommendation system that utilizes advanced machine learning techniques to suggest movies similar to the user's preferences and watching history.
An API based movie app where the user can see available movies, comment and also like a particular movie. Built with JavaScript / SASS / Webpack / Bootstrap / CSS3 and HTML5
This Movie Recommendation System takes a movie name as an input and recommends you some movies like the input movie based on similarity between them. It also shows you information about input movie and the details of cast who have acted in it.
Choose a category and algorithm suggests you a new movie to watch today.
영화 감상 반응(집중도/감정)을 분석하고 이를 기반으로 취향을 분석해 영화를 추천하는 서비스 (Front)
A simple api serving movie based on emotions
A survey of deep learning-based movie recommendation systems
Among the six Star Wars movies released between 1977 and 2003; which is the most favorite and what is the order of movie preference from first to sixth among the viewers surveyed?
Created a movie recommender system using IMDB database, Pyhton and Streamlit.
Making a movie recommendation to the user by interacting with the user in the web interface of the model created using machine learning with Python, in the web interface made with Django-MySQL.
Movie Recommendation System Web app
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