A simple implementation of a Movie recommendation system using collaborative filtering
The objective of collaborative filtering is to predict movie ratings for the movies that users have not yet rated. This will allow us to recommend the movies with the highest predicted ratings to the user.
The matrix Y (a num movies × num users matrix) stores the ratings y(i,j) (from 1 to 5). The matrix R is an binary-valued indicator matrix, where R(i, j) = 1 if user j gave a rating to movie i, and R(i, j) = 0 otherwise.
You can enter your own movie preferences, so that later when the algorithm runs, you can get your own movie recommendations! Some movies have been ranked based as an example on how to do it, but you should change this according to your own tastes.
To update your preferences, edit code\movie_recommender.m, line 22, add a line
my_ratings(movie_idx) = 3;Where movie_idx is the index of the movie you want to rate 3/5.
The list of all movies and their number in the dataset can be found listed in the file data\movie_ids.txt
Then, to get your recommendations, simply run the script code\movie_recommender.m, the recommendations
are outputted in the console.
This project was part of Andrew Ng's Mooc on machine learning which I strongly recommend.
This project is no longer updated.