Recommender systems

Music Recommendation System

Github Link: github.com/MetinUnlu/music-recommendation

Final project for Mining Massive Datasets course at Verona University

This comprehensive music recommendation system demonstrates the implementation and comparison of multiple recommendation approaches on a large-scale dataset. The project tackles the challenge of processing over 3.6 million user-music interactions from the Million Song Dataset combined with Spotify and Last.fm data to deliver personalized music recommendations.

Project Overview

Music recommendation systems play a crucial role in modern streaming platforms, helping users discover new content based on their listening history and preferences. This project explores both collaborative filtering and content-based recommendation techniques, addressing common challenges such as the cold start problem, data sparsity, and scalability issues inherent in large-scale recommendation systems.

Dataset and Data Processing

The project utilizes the comprehensive Million Song Dataset enhanced with Spotify and Last.fm data:

Data Preprocessing Strategy

A sophisticated normalization approach was implemented:

Methodology and Implementation

1. Collaborative Filtering Approaches

Multiple collaborative filtering techniques were implemented and compared:

Koren Neighborhood Model

Surprise Library Implementation

LightFM Recommender

Implicit Library (Primary Method)

The core recommendation engine utilizes the Implicit library, chosen for its superior performance:

2. Content-Based Filtering

A k-Nearest Neighbors cosine similarity approach was implemented:

3. Hybrid Recommendation Strategy

An intelligent hybrid approach combines both methods based on user behavior:

Technical Implementation Details

Model Architecture

The system implements a modular architecture with the following components:

Performance Optimization

Results and Performance Analysis

Model Performance Comparison

Real-World Recommendation Examples

The system generates recommendations by combining collaborative filtering and content-based filtering to match both user preferences and musical similarity. For example, for a user with an alternative/indie and electronic music taste, the following tracks are recommended:

Collaborative filtering recommendations (e.g., The Velvet Underground, New Order, Arcade Fire) are based on users with similar listening patterns, capturing shared music tastes such as alternative rock and indie genres. Content-based recommendations (e.g., Eisbrecher, Noir Désir, MSTRKRFT, ATB) are selected for their audio features, genre tags, and stylistic similarity to the user's previous favorites, ensuring diversity and relevance even for less common preferences. This hybrid approach ensures the user receives both familiar and novel tracks that align with their unique musical profile.

Cold Start Problem Mitigation

The hybrid approach effectively addresses the cold start problem:

Technical Challenges and Solutions

Scalability Challenges

Data Sparsity Issues

Technologies and Libraries

Key Insights and Learnings

Algorithm Performance Insights

Practical Implementation Lessons

Future Enhancements

Academic Contribution

This project demonstrates a comprehensive understanding of recommendation system principles and their practical implementation at scale. The comparative analysis of multiple algorithms, intelligent hybrid approach, and successful handling of real-world challenges like data sparsity and cold start problems showcase both theoretical knowledge and practical engineering skills essential in modern data science applications.

  • Recommendation Systems
  • Collaborative Filtering
  • Matrix Factorization
  • Content-Based Filtering
  • Implicit Feedback
  • Sparse Matrices
  • Hybrid Systems
  • Large-Scale ML