thegenuinegourav/movies-recommender

A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm.

Movies Recommender

A system to recommend movies according to ratings provided by users using Collaborative Filtering Learning Algorithm.

Description 📒

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.

How it works ❓

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).

Output

Development

Want to contribute? 📝

To fix a bug or enhance an existing module, follow these steps:

1. Fork the repo
2. Create a new branch (git checkout -b exciting-stuff)
3. Make the appropriate changes in the files
4. Add changes to reflect the changes made
5. Commit your changes (git commit -am 'exciting-stuff!!')
6. Push to the branch (git push origin exciting-stuff)
7. Create a Pull Request

Interested?

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.

How to Build

• launch Octave/Matlab
• select Open command window
• select the project folder
• type 'recommender' on command window

Articles to Learn

• https://blog.dominodatalab.com/recommender-systems-collaborative-filtering/
• https://en.wikipedia.org/wiki/Collaborative_filtering
• https://gist.github.com/obstschale/7320846  

movies-recommender's Issues

i want to build a recommender system using python, are you interested to help?

does the project use one of machine learning algorithms?

Predicted rating higher than 5

If you comment out following lines of recommender.m, the predicted rating exceeds 5 which is obviously not correct.

Y = [my_ratings Y];
R = [(my_ratings ~= 0) R];