Game recommendation website

Table of contents

1. Introduction
2. Requirements
3. Installation
4. Usage

Introduction

The goal of this repository is to implement one of the game recommendation methods developed in the Game recommendation system project. Specifically, it uses a nearest neighbors approach to generate recommendations based on user similarity. Despite not being the most effective method from the comparative analysis, this method can generate recommendations in a short time manner, making it suitable for a web application. The website is available here.

This implementation is presented in the form of a simple web application that allows users to generate recommendations by providing a link to their Steam profile. The app processes the user's game data and generates recommendations based on user similarity, offering a practical demonstration of the methodology described in the original project.

Requirements

• Python 3.11.11
• conda
• Django
• dotenv
• requests
• pandas
• numpy

Installation

1. Clone the repository:

git clone https://github.com/Eniterusx/Game-recommendation-website
cd Game-recommendation-website

2. Create a conda environment and install the required packages:

conda env create -f requirements.yaml
conda activate game-rec-web

Usage

1. Create a .env file with the following content:

API_KEY=your_steam_api_key

The API_KEY is the Steam API key that you can get here.

2. Run the Django server:

python manage.py runserver

3. The application is now available at http://127.0.0.1:8000/.