Apache Spark Programs to perform data analysis on movielens data
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Updated
Jan 19, 2016 - XSLT
Apache Spark Programs to perform data analysis on movielens data
This is an intro to recommender systems using the popular Movielens dataset and Spark.ML
Data processing with movieLens dataset
This is movie recommendation system with pandas back-end. There are a few things you can do with it. Search for movie, find movie what to watch based on genre and when you have watched a movie to find other movies similar to it.
Basic Recommender System that provides Content Based Filtering or Product based Recommendations over MovieLens Movies data-set to be used with native and AWS EMR Hadoop
A simple project to try and evaluate likely.js recommendations system.
Implementation of various popular Collaborative Filtering algorithms in Python.
A recommendation algorithm using the MovieLens dataset.
Item-based recommender system on MovieLens data
GPGPU Parallel User-User Collaborative Filtering System in CUDA C
In this notebook, I have created an example how the movies are recommended to a user based on contents. I have used data from the popular MovieLens website.
Recommends movies based on user's current favourite movie.
Movie Recommendation System using the MovieLens dataset
Homework assignments for CS657 mining massive datasets. Assignments are in Spark and Hadoop using the Python API. Assignments include wordcount stuff, association rule mining, linear regression, and recommender systems.
PySpark implementation of the probabilistic latent semantic indexing algorithm
Basic Implementation of Recommender System.
Conducting a study of a recommendation system based on ALS based on Movielens movie data.
A movie recommendation demo that uses the LightFM library and the movielens dataset.
The system acts a ratings generator using live twitter feed to evaluate the rating of a movie as well as a recommendation engine to recommend movies to the users based a dataset.
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