This project aims at building a movie recommender with a Flask web interface. You can visit the website at this link. Please note that it might take up to 1 min to display the result page (i have limited resources in the free plan).
The dataset of movies and ratings I will be using is the movielens dataset available at:
https://grouplens.org/datasets/movielens/
I provide a sqlite database at data/movielens.db in order to run the program (this section can be skipped to test the recommender).
To update it regularly including new movies (or create your own database), download movies.csv and ratings.csv from https://grouplens.org/datasets/movielens/ and place them in the folder data.
Then run python prepare_database.py (make sure you delete the old database before).
It will extract the years from movies title, clean the title with regex and merge them into a database containing
['userId','movieId','rating','title','genres','year','timestamp']. Then it will be saved as a sqlite database
in the folder data.
In the code there is the option to save it as a postgres database as well (turn Save_to_postgres to True).
By default it will only be saved as a sqlite database (because it is the option i used to deploy on the web at pythonanywhere with the free plan).
Below, a word cloud created based on the appearance of movies genres in the database:
The python file that will house the instructions for launching a web server is
available at application.py
It consists in three functions index, recommender and results related to the templates
index.html, recommender.html and results.html provided in templates folder.
The index page will welcome the user and invite him to access the recommender page. In the recommender page,
the user will be asked to fill a form and rate 5 movies of his choice. These movies and ratings will be passed
to the results function via request.args. Then, the Recommender class (see next section for more details)
will be instantiate with these values and will predict the 5 movies to recommend. These movies will then be
printed in the results.html page.
To visit your website locally, run bash ./run_server.sh and then open http://127.0.0.1:5000/ in your
browser. It should take some seconds to return the recommended movies.
The Recommender class available at recommender_tools.py can predict movies recommendations
based on Non-negative matrix factorization (NMF) or cosine similarity.
The Recommender class is instantiated with the values provided by the user. It then reads the local sqlite database and search the correspondence between the title provided by the user and the titles in the database (with the help of fuzzywuzzy package that uses Levenshtein distance to calculate the differences between sequences). It creates a new vector of movies ratings for the user (a vector containing all the movies Ids of the database and ratings for the movies the user chose).
It then calls the prediction function to return the recommended movies.
There is two prediction functions prediction_nmf and prediction_cosim.
prediction_nmf will predict the movies based on Non-negative matrix factorization of the Matrix
(user_id, movie_id, ratings). A saved model is provided at data/nmf_model_rating.bin , but if
not present in the folder, the class will train a model from scratch using the database (it might be useful to update the model regularly when updating the database) and save it to disk.
prediction_cosim will predict the movies based on cosine similarity between the new vector of movies ratings
by the user and the movies ratings of all users of the database.
It will select the three closest users from the database and pick up 5 of the best rated movies by these users(excluding the movies already rated by the new user).
By default, the Recommender class will predict with the cosine similarity method (it was performing better
than NMF on tests for new user). But you can easily called the NMF prediction instead by calling the
prediction_nmf method of the Recommender class.
Clone this repository
pip install -r requirements.txt
bash ./run_server.sh
Visit at http://127.0.0.1:5000/
