Music_Recommendation_System

1. Music Recommendation System
• An overview of intelligent systems that
suggest music to users based on data analysis.

2. 1. Introduction
• Software that suggests songs, artists, or
albums.
• Essential for user engagement on digital
platforms.

3. 2. Objective
• Analyze user preferences and listening
patterns.
• Provide personalized music recommendations.

4. 3. Types of Recommendation
Systems
• Content-Based Filtering: Based on song
metadata.
• Collaborative Filtering: Based on user
behavior.
• Hybrid Systems: Combine both approaches.

5. 4. System Architecture
• Data Collection & Preprocessing.
• Modeling using ML/DL algorithms.
• Recommendation Engine.
• Evaluation with metrics like precision and
recall.

6. 5. Technologies Used
• Languages: Python, JavaScript.
• Libraries: Pandas, NumPy, Scikit-learn,
TensorFlow, Flask.
• Databases: PostgreSQL, MongoDB.
• Tools: Jupyter Notebook, Google Colab,
Docker.

7. 6. Challenges
• Cold start problem.
• Scalability issues.
• Data sparsity.
• Balancing diversity and relevance.

8. 7. Conclusion
• Enhances user experience with personalized
music.
• Uses advanced algorithms and real-time data
analysis.

9. 8. Future Work
• Emotion-based recommendations.
• Use of contextual data (time, location,
activity).
• Real-time user preference adaptation.

10. 9. References
• Ricci, F., Rokach, L., & Shapira, B. (2015).
• Spotify Research Blog.
• ACM Digital Library.
• IEEE Xplore.