Enhancing-Personalized-Music-Recommendations

This project implements a hybrid music recommendation system that combines collaborative filtering and content-based filtering to enhance personalized music experiences for users. It aims to balance user preferences with content similarity, delivering more accurate and engaging song suggestions.

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🔍 Key Features

• Collaborative filtering using user-item interactions
• Content-based filtering via audio features or metadata
• Hybrid scoring mechanism for final recommendations
• Evaluation using precision, recall, and diversity metrics

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🛠️ Setup & Usage

1. Clone the repository

    git clone https://github.com/MS134340/Enhancing-Personalized-Music-Recommendations
    cd Enhancing-Personalized-Music-Recommendations
   

2. Install required packages

   pip install -r requirements.txt

3. Run the notebook Open and execute the .ipynb file in Jupyter or Google Colab.

📊 Results

• Improved recommendation accuracy vs standalone methods.
• Better user retention and diversity in music suggestions.