An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
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Updated
Oct 6, 2021 - Jupyter Notebook
An on-line movie recommender using Spark, Python Flask, and the MovieLens dataset
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
Simplified version of IMDb
The Movie Database for all language movies
使用机器学习算法的电影推荐系统以及票房预测系统
Basic Movie Recommendation Web Application using user-item collaborative filtering.
🎥 Everything about your movies within the command line.
🍃 Recommender System in JavaScript for the MovieLens Database
Pick your next movie using Next.js 13
Data pipeline performing ETL to AWS Redshift using Spark, orchestrated with Apache Airflow
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.
Content based movie recommendation system with sentiment analysis
Movie Recommendation System with Complete End-to-End Pipeline, Model Intregration & Web Application Hosted. It will also help you build similar projects.
Python操作Neo4j数据库,知识图谱,根据相似度计算的一个电影推荐的Demo
Movies Reviewed by people, for people
Movie Recommender System with Django.
使用MovieLens数据集实现了基于Auto Encoder(AE), Variational Auto Encoder(VAE), BERT的深度学习电影推荐系统
Theatherflix Extension is a browser extension that provides personalized movie and series recommendations to users. Using The Movie Database (TMDb) API, the extension fetches popular movie data and displays customized suggestions based on user preferences.
Personalized real-time movie recommendation system
Contains code which covers various methods for recommending movies, some of the methods include matrix factorisation , deep learning based recommendation systems
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