gr1nch3/user_recommendation

User Recommendation System

This is a Django-based web application for a user recommendation system, which uses PostgreSQL as the database and provides a REST API endpoint for user recommendations.

Table of Contents

• Getting Started
• Prerequisites
• Installation
• Running the Application
• Running Tests
• Environment Variables
• Contributing
• License

Getting Started

Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

• Docker installed on your machine.
• Docker Compose installed on your machine.

Installation

1. Clone the repository:

   git clone https://github.com/gr1nch3/user_recommendation.git
   cd user_recommendation

2. Set up environment variables:

   Create a .env file in the root directory of the project and add the following environment variables:

   POSTGRES_HOST=db
   POSTGRES_PORT=5432
   POSTGRES_DB=recdb
   POSTGRES_USER=postgres
   POSTGRES_PASSWORD=insecure-postgres
   DJANGO_DEBUG=True

Running the Application

1. Build and run the Docker containers:

   docker compose up build

   This command will build the Docker images and start the containers for the web application, PostgreSQL database, and pgAdmin.

2. Apply database migrations:

   docker compose run --rm web python manage.py migrate

3. Import Data to the database: There are .txt files that contains filtered data in the filtered/* folder. There is a file for user, tweet and a hashtag table for relational purposes. To populate the data in the database, use the following commands:

   # command to populate the user database (do this first)
   docker compose exec web python manage.py populate_users
   
   # next command to populate the tweets
   docker compose exec web python manage.py populate_tweets
   
   # command to populate the hashtags
   docker compose exec web python manage.py populate_hashtags
   
   # command to add popular hashtags to ensure better recomendation
   docker compose exec web python manage.py update_hashtags

   The malformed_duplicate_filter.py file contains code to generate the files in the filtered folder from the query2_ref.txt file in the challenge/dataset/* folder. You can delete the files from the filtered folder and generate new ones should you ever feel the need to.

4. Access the application:

   The application will be accessible at http://localhost:8000. The endpoint for the user recommendation test should be like:

   GET http://localhost:8000/q2?user_id=102482331&type=both&phrase=una&hashtag=%23RE
   

5. Access pgAdmin:

   pgAdmin will be accessible at http://localhost:5050. Use the following credentials to log in:

   • Email: [email protected]
   • Password: admin

Running Tests

To run the tests, execute the following command:

docker compose exec web python manage.py test