Development of an Intelligent Music Recommendation System Using Machine Learning

Users of digital music streaming services face difficulty in efficiently discovering new music that aligns with their preferences amidst an overwhelming volume of available tracks. Existing recommendation approaches lack sufficient personalization, leading to reduced user engagement and satisfaction. The client seeks to improve the quality of music recommendations by leveraging advanced data analysis and machine learning techniques to better understand individual user tastes and behaviors.

A digital music streaming platform aiming to enhance user engagement and music discovery through personalized recommendations.

• Develop a scalable music recommendation system that delivers highly personalized song suggestions based on user listening history and preferences.
• Implement collaborative filtering and machine learning algorithms to identify user behavior patterns and improve recommendation accuracy.
• Create an intuitive web interface allowing users to specify their music preferences and receive real-time suggestions.
• Integrate preview features enabling users to listen to song snippets directly within the platform.
• Achieve measurable improvements in user engagement metrics, such as increased session duration and playlist diversity.

• User data collection module capturing listening history, ratings, and exploration queries.
• Recommendation engine utilizing collaborative filtering and machine learning algorithms to generate personalized suggestions.
• User interface allowing selection of music attributes, preference input, and recommendation display.
• Real-time playlist generation and updating based on user interactions.
• Music preview feature enabling users to listen to track snippets before selection.
• Data visualization tools for user preferences and system recommendations.

• Streaming platform APIs for retrieving user listening data and song metadata.
• Music preview services for audio snippets.
• User authentication and account management systems.

• Scalability to handle increasing user base and music library size.
• Low latency response times to support real-time recommendations (target < 2 seconds per request).
• Robust security measures to protect user data and privacy.
• High system availability with 99.9% uptime.

By implementing an advanced personalized music recommendation system, the platform is expected to significantly enhance user engagement, increase session duration, and improve user retention. Target metrics include a 25% increase in active user sessions and higher diversity in user playlists, ultimately driving higher subscription rates and revenue growth.