A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user
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Updated
Jan 22, 2024 - Jupyter Notebook
A content-based recommender system that recommends movies similar to the movie the user likes and analyses the sentiments of the reviews given by the user
An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
The Movie Database for all language movies
Basic Movie Recommendation Web Application using user-item collaborative filtering.
The purpose of our research is to study reinforcement learning approaches to building a movie recommender system. We formulate the problem of interactive recommendation as a contextual multi-armed bandit.
使用机器学习算法的电影推荐系统以及票房预测系统
Movie Recommender System with Django.
Movie Recommendation System with Complete End-to-End Pipeline, Model Intregration & Web Application Hosted. It will also help you build similar projects.
🍃 Recommender System in JavaScript for the MovieLens Database
Content based movie recommendation system with sentiment analysis
Movie Recommendation System: Project using R and Machine learning
This is a simple movie recommendation modal deployed on Heroku.
Data pipeline performing ETL to AWS Redshift using Spark, orchestrated with Apache Airflow
It is a movie recommender web application which is developed using the Python.
Movies Reviewed by people, for people
Contains code which covers various methods for recommending movies, some of the methods include matrix factorisation , deep learning based recommendation systems
Free movie and TV streaming website made with HTML, CSS, and vanilla JavaScript.
Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.
This is the practice of making movie recommendation engines.
Simplified version of IMDb built with React
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