Mu'een — Project Recommendation System

Mu'een is an intelligent graduation project recommendation system for CCIS at Imam Mohammad Ibn Saud Islamic University.

It recommends suitable graduation projects for student groups based on:

• Academic performance (courses & grades)
• Research interests
• Application domains
• RDIA priorities

The system uses semantic embeddings and AI-powered summarization to deliver explainable and personalized recommendations.

※ Mu'een Features

❊ Intelligent project recommendation using semantic similarity
❊ Group-based recommendation system
❊ Trend analysis of graduation projects
❊ AI-generated project summaries
❊ Advanced filtering and project browsing
❊ Adjustable recommendation weighting system

※ How Mu'een Works

1. Students complete their academic profile
2. System aggregates group preferences
3. Semantic embeddings are used to match projects
4. Recommendation engine ranks projects
5. AI generates explanations and summaries

※ Core Recommendation Engine

The recommendation logic powering Mu'een is documented in detail in a dedicated repository:
GP-Recommender

This covers the full technical breakdown of the embedding strategy, hybrid retrieval pipeline, group profile construction, and domain scoring logic that forms the basis of this system.

※ System Screenshots

Recommended Projects

Trend Analysis

Browse Projects

Project Structure

project-recommender/
├── backend/              # Flask API server
│   ├── app.py            # Main application entry point
│   ├── database.py       # SQLite database layer
│   ├── embedding_engine.py
│   ├── models.py
│   ├── phase2_embed.py   # One-time embedding generation script
│   ├── requirements.txt
│   ├── summarizer.py
│   ├── trend/            # Trend analysis module
│   └── utils.py
├── data/                 # Domain taxonomy and project JSON files
├── embeddings/           # Pre-computed project and course embeddings
├── frontend/             # React + TypeScript frontend (Vite)
│   └── src/
│       ├── components/   # Page and UI components
│       ├── contexts/     # Auth context
│       └── services/     # Axios API client
├── recommenders/         # Recommendation logic modules
├── recommender_system.py # Main recommender orchestrator
└── RS_Evaluation/        # Evaluation scripts and results

Prerequisites

• Python 3.10+
• Node.js 18+

Setup and Run

Backend

cd backend
pip install -r requirements.txt
python app.py

The API server starts at http://localhost:5000.

Frontend

In a separate terminal:

cd frontend
npm install
npm run dev

The app opens at http://localhost:3000.

Using a virtual environment (recommended)

python -m venv venv
# Windows
venv\Scripts\activate
# macOS / Linux
source venv/bin/activate

cd backend
pip install -r requirements.txt
python app.py

Notes

• Embeddings are pre-computed and included in the embeddings/ directory. If you add new projects, run python backend/phase2_embed.py to regenerate them.
• The database (backend/recommendation.db) is created automatically on first run.
• An OpenAI API key is required for the project summary feature. Set it as the OPENAI_API_KEY environment variable before starting the backend.

Team

Mu'een Project Team

Name	ID
Abeer Hasan Othman	443019197
Dhekra Adel Dabwan	443520332
Ohood Mohammed Al-Magedi	443520331
Shahad Abdullah Baelaian	443019212

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Supervisor

Dr. Waad Alhoshan, PhD