https://github.com/farhard112/spider-v1-game-recommender-system

AI-powered game recommendation system using KNN and collaborative filtering, with Flask and C# APIs and an Angular frontend
https://github.com/farhard112/spider-v1-game-recommender-system

ai angular asp-net-core collaborative-filtering collaborative-filtering-algorithm collaborative-filtering-based-recommendation csharp docker docker-container flask game-recommender knn machine-learning microservices recommender-system rest-api

Last synced: 8 days ago
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AI-powered game recommendation system using KNN and collaborative filtering, with Flask and C# APIs and an Angular frontend

• Host: GitHub
• URL: https://github.com/farhard112/spider-v1-game-recommender-system
• Owner: FarHard112
• Created: 2024-08-20T19:23:23.000Z (about 1 month ago)
• Default Branch: master
• Last Pushed: 2024-09-24T19:56:02.000Z (9 days ago)
• Last Synced: 2024-09-26T02:04:59.529Z (8 days ago)
• Topics: ai, angular, asp-net-core, collaborative-filtering, collaborative-filtering-algorithm, collaborative-filtering-based-recommendation, csharp, docker, docker-container, flask, game-recommender, knn, machine-learning, microservices, recommender-system, rest-api
• Language: TypeScript
• Homepage:
• Size: 491 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        

# Spider Game Recommender

An AI-powered game recommendation system that leverages collaborative filtering and K-Nearest Neighbors (KNN) to provide personalized game suggestions based on user behavior and preferences.

## Overview

The Spider Game Recommender is a comprehensive solution that combines:

-   A Flask API serving AI models for game recommendations

-   A C# backend API for handling user interactions and data management

-   An Angular frontend for a seamless user experience

  ![image](https://github.com/user-attachments/assets/a7cf4093-2e48-490c-8c42-16d3b9849923)

## Features

-   Personalized game recommendations using collaborative filtering and content-based methods

-   Real-time API for dynamic suggestions

-   Integration with Steam for game images

-   User profile management and favorite game tracking

-   Dockerized deployment for easy scaling and consistency

## Technical Stack

-   **AI/ML**: Python, scikit-learn, pandas

-   **Backend**: Flask (Python), ASP.NET Core (C#)

-   **Frontend**: Angular

-   **Deployment**: Docker, Contabo Linux Ubuntu server

## Key Components

1.  **Data Processing Pipeline**

    -   Data ingestion from multiple sources

    -   Feature extraction using TF-IDF vectorization

    -   KNN model training for content-based filtering

2.  **Recommendation Engine**

    -   Collaborative filtering for user-based recommendations

    -   Content-based filtering using game features

3.  **APIs**

    -   Flask API for serving ML models

    -   C# API for user management and application logic

4.  **Web Application**

    -   Angular-based frontend for user interactions

    -   Display of game recommendations with images

## Deployment

The system is deployed on a Contabo Linux Ubuntu server using Docker containers for both the Flask and C# APIs.

![WhatsApp Image 2024-06-13 at 10 36 06 PM](https://github.com/user-attachments/assets/7369d2ab-2d65-4cb2-a89e-5b8f22937fc2)

## API Documentation

API documentation is available through Swagger UI. Key endpoints include:

-   `GET /api/Home/recommendations`: Fetch game recommendations

-   `PUT /api/Home/update-favorite/{id}`: Update user's favorite games

-   `POST /api/Home/get-collaborative-filter-recommends`: Get recommendations using collaborative filtering

## Future Improvements

-   Implement model retraining capabilities

-   Enhance error handling and input validation

-   Implement logging for performance monitoring

-   Explore more complex models or ensemble methods for improved accuracy