Build a Hybrid Recommendation System for an Indie Streaming Catalog
Overview
What this challenge is about.
Build a hybrid recommender with ALS and audio embeddings for a streaming catalog, then evaluate and ship it for A/B testing. Earn a verifiable certificate.
The scenario
The streaming service (Series B, around USD 18M ARR) has 3 weeks of runway in the discovery-retention metric before the board questions the strategy.
The Brief
What you'll do, and what you'll demonstrate.
Ship a hybrid recommender that beats the production collaborative-filtering baseline on NDCG@10 and improves long-tail catalog coverage in a 2-week A/B test.
Earning criteria — what you'll demonstrate
- Train and evaluate ALS, content-based, and hybrid recommenders end-to-end
- Use audio embeddings as content features for cold-start tracks
- Reason about catalog coverage and diversity alongside ranking metrics
- Read an A/B test result without overclaiming early lift
Program Fit
Where this fits in your program.
Sharpens the same skills your degree expects you to demonstrate.
Aligned coursework coming soon.
Skills
Skills you'll demonstrate.
Each one shows up on your verified credential.
- Recommendation Systems
Apply recommendation systems to solve real industry problems and demonstrate production-level capability.
- Collaborative Filtering
Apply collaborative filtering to solve real industry problems and demonstrate production-level capability.
- Content Based Recommendation
Apply content based recommendation to solve real industry problems and demonstrate production-level capability.
- Ndcg Evaluation
Apply ndcg evaluation to solve real industry problems and demonstrate production-level capability.
- Ab Testing
Apply ab testing to solve real industry problems and demonstrate production-level capability.
- Embedding Models
Apply embedding models to solve real industry problems and demonstrate production-level capability.
Careers
Career paths this challenge builds toward
Completing this challenge demonstrates skills that transfer directly to these roles:
Product Manager
Product managers who can read NDCG and coverage trade-offs make the discovery-retention calls without deferring to data science.
This challenge sharpens
- ndcg-evaluation
- ab-testing
- recommendation-systems