Building a Large Music Recommender Leveraging AI, Deep Learning and Human Expertise

Overview

Discover the intricacies of Pandora’s cutting-edge music recommendation system, a synergy of artificial intelligence, deep learning, and human expertise. This comprehensive framework is designed to create personalized listening experiences, drawing on extensive user data and profound musicological insights.

By attending this session, gain valuable insights into how Pandora leverages these technologies to understand musical preferences, enhancing user engagement through curated playlists and recommendations that resonate with individual tastes.

Whether you're an AI enthusiast, a musicologist, or simply curious about the tech behind your favorite tunes, this talk provides a captivating look into the future of music discovery.

Syllabus

• Introduction to Music Recommendation Systems
Overview of Music Recommender Systems
The Role of AI in Music Recommendations
Pandora as a Case Study
• Fundamentals of AI and Deep Learning in Recommender Systems
Basics of AI and Machine Learning
Introduction to Deep Learning
Neural Networks in Practice
• Data Collection and Processing
Types of User Data in Music Recommendation
Sources of Musicological Data
Techniques for Data Cleansing and Preprocessing
• Building User Profiles and Preferences
Understanding User Behavior
Segmentation and Clustering Techniques
Feature Engineering for Recommender Systems
• Deep Learning Architectures for Music Recommendations
Collaborative Filtering vs. Content-Based Filtering
Implementing Deep Neural Networks
Convolutional Neural Networks (CNNs) for Audio Analysis
• Integrating Human Expertise
Role of Musicologists in Recommender Systems
Combining AI with Human-Curated Data
Balancing Algorithmic and Human Insights
• Personalization and User Experience
Techniques for Personalized Recommendations
User Interface Design Considerations
Metrics for Evaluating User Satisfaction
• Advanced Topics in Music Recommendation
Context-Aware and Sequence-Based Recommendations
Reinforcement Learning in Music Recommenders
Addressing Bias and Fairness
• Case Study: Pandora’s Music Genome Project
Overview of the Music Genome Project
Implementation of AI and Deep Learning
Lessons Learned and Challenges Overcome
• Practical Implementation and Deployment
Building a Prototype Music Recommender
Tools and Technologies in Use
Deployment and Scaling Strategies
• Capstone Project
Design and Develop a Personalized Music Recommender System
Present Findings and Solutions
• Conclusion and Future Directions in Music Recommendations
Emerging Trends in AI and Music Technology
Future Challenges and Opportunities
• Resources and Further Reading
Key Textbooks and Articles
Online Courses and Workshops
Industry Insights and Conferences

---

Subjects

Conference Talks