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Development of an AI-Powered Music Recommendation System with Collaborative Filtering

saigontechnology.com

Information technology

Media

eCommerce

Challenge in Music Discovery

Digital music streaming platforms face exponential growth in content libraries, creating user overwhelm and difficulty discovering music aligned with personal tastes. Current systems lack sophisticated personalization capabilities, leading to reduced user engagement and satisfaction.

About the Client

Research and development laboratory specializing in AI/ML solutions for digital media and consumer platforms

Core Project Goals

Implement machine learning algorithms for personalized music recommendations
Develop collaborative filtering system to identify user behavior patterns
Create intuitive web interface for user interaction and preference input
Integrate audio analysis with user preference data for hybrid recommendations

System Functionality Requirements

User preference input interface with attribute selection
Real-time recommendation engine using collaborative filtering
Audio preview playback functionality
User profile creation and history tracking
Interactive Streamlit-based web application

Technology Stack

Python programming language

NumPy/Pandas for data processing

Scikit-learn for ML algorithms

Streamlit framework for UI

Natural Language Processing (NLP) techniques

System Integrations

Music metadata APIs (e.g., Spotify/Last.fm)
Audio processing libraries (e.g., Librosa)
User authentication services
Cloud storage for user preference data

Performance Criteria

Scalable architecture for 100k+ concurrent users
Recommendation generation latency <200ms
99.9% system uptime SLA
GDPR-compliant data handling
Cross-platform mobile responsiveness

Business Value Projections

Implementation of this system is projected to increase user engagement metrics by 40% through improved music discovery, reduce churn rates by 25% via personalized experiences, and enhance platform stickiness by creating unique value propositions in competitive streaming markets.