Personalization at Scale Using Recommendation Systems
Delivering hyper-personalized music discovery through behavioral ML models
The Challenge
Our Approach
We built a hybrid recommendation engine combining collaborative filtering with content-based and deep learning models. Core components included:
- Matrix factorization using implicit feedback (Spark ALS)
- NLP-powered song tagging using BERT for lyrics and genre embeddings
- Session-based RNN models to capture short-term listening patterns
- Online learning strategies for real-time personalization
The model output was A/B tested via a reinforcement learning loop to optimize for engagement metrics.
Stats
The Outcome
- 42% boost in daily active users
- 55% increase in personalized playlist consumption
- 2.1x better retention for new users within 14 days
- Real-time recommendations updated with <1 second latency
Case Study
- All Posts
- AI Agents
- AI Chat Bot
- AI Kickstater
- Computer Vision
- Data Engineering
- Gen AI
- Machine Learning
Home Gen AI Global Invesment Firm Global Investment Firm Transforming regulatory research with generative AI for faster, smarter compliance insights...
Home Machine Learning Global Insurance Provider From Risk Assessment to Risk Prediction with ML Powering smarter underwriting with machine learning–based...
Home Machine Learning Smart Manufacturing Firm Predictive Maintenance with Machine Learning on the Edge Driving efficiency in factories with predictive...
