recommender

Recommender system for movies. Combination of static metrics and visual review of recommendations.

Includes the following algorithms:

• Recommendations by popularity
• Recommendations by movie descrition
• Item/Item collaborative filtering
• User/User collaborative filtering
• Regularized SVD

Project Setup

In the following, we will go through the steps to set up this system.

The first thing is to download this repository. Secondly, create a themoviedb.org ID needed to run the website.

Download source code

You have two choices for downloading the source code – downloading a zip file of the source code or using Git.

• Downloading a zip file

  From the main directory on GitHub, click the green “Clone or download” button and choose to download a zip file to your computer.

• Using Git

  Clone this repository or create a fork in your GitHub, and then clone that instead. The following command will create a copy on your computer. > git clone https://github.com/jug2505/recommender.git

Create a virtual environment for the project

Before you run the code, create a virtual environment.

> cd back
> virtualenv -p python3 venv
> source venv/bin/activate

Get the required packages

pip3 install -r requirements.txt

Configuration Django for PostGreSql connection

Open rs_project/settings.py

Update this lines:

DATABASES = {
    'default': {
        'ENGINE': 'django.db.backends.postgresql',
        'NAME': 'moviegeek',                      
        'USER': 'db_user',
        'PASSWORD': 'db_user_password',
        'HOST': 'db_host',
        'PORT': 'db_port_number',
    }
}

Run the following commands:

> python3 manage.py makemigrations
> python3 manage.py migrate --run-syncdb

Populate the database

Run the following scripts to download the datasets for the system.

> python3 -m scripts.dataset_downloaders.movies_downloader
> python3 -m scripts.dataset_downloaders.raitings_downloader
> python3 -m scripts.dataset_downloaders.description_downloader

Calculate recommendations

Run all scripts in scripts/calculators folder.

Calculate static metrics

Run scripts/statistic/metrics.py script. It includes RMSE and Precision at K metrics.

DB service run:

sudo service postgresql start

Django server run

> python3 manage.py runserver 127.0.0.1:8081

Vue.js project setup

npm install

Vue.js developer server

npm run serve