A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm.
This system will implement the collaborative filtering learning algorithm and apply it to a dataset of movie ratings.
This dataset consists of ratings on a scale of 1 to 5. The dataset has n(u) = 943 users, and n(m) = 1682 movies.
The matrix Y (a num movies X 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.
The objective of collaborative filtering is to predict movie ratings for the movies that users have not yet rated, that is, the entries
with R(i,j) = 0.
This will allow us to recommend the movies with the highest predicted ratings to the user.
Step 1: Modify 'recommender' script to input your own ratings against different movies.
Step 2: Run 'recommender' script in your Octave/Matlab command window.
Step 3: This run 100 iterations, first to train & then outputs the movies best suited for you (recommended).
Want to contribute? 📝
To fix a bug or enhance an existing module, follow these steps:
- Fork the repo
- Create a new branch (
git checkout -b exciting-stuff) - Make the appropriate changes in the files
- Add changes to reflect the changes made
- Commit your changes (
git commit -am 'exciting-stuff!!') - Push to the branch (
git push origin exciting-stuff) - Create a Pull Request
If you find a bug (the system couldn't handle the query and / or gave irrelevant results), kindly open an issue here by including your search query and the expected result.
If you'd like to request a new functionality, feel free to do so by opening an issue here including some sample queries and their corresponding results.
- launch Octave/Matlab
- select Open command window
- select the project folder
- type 'recommender' on command window