Building Recommender Systems
A comprehensive 3-part series on building production-grade recommender systems from scratch
What You'll Learn
This series takes you from recommender system fundamentals to advanced production techniques. Each part builds on the previous, with complete Python implementations using real datasets.
Part 1: Foundations: Content-Based & Collaborative Filtering
Learn the fundamentals of recommender systems, build content-based and collaborative filtering algorithms from scratch with Python.
Topics Covered
- ✓What is a recommender system?
- ✓Content-based filtering with TF-IDF
- ✓User-based collaborative filtering
- ✓Item-based collaborative filtering
- ✓Evaluation metrics (RMSE, Precision@K, NDCG)
Part 2: Matrix Factorization & Factorization Machines
Scale recommendations with SVD, ALS, and Factorization Machines. Learn production optimization techniques used at Netflix and Spotify.
Topics Covered
- ✓Matrix Factorization (SVD, ALS)
- ✓Latent factor models
- ✓Factorization Machines for side features
- ✓Implicit feedback handling
- ✓Production optimizations (ANN, caching)
Part 3: Deep Learning & Production Systems
Coming soon: Neural collaborative filtering, session-based recommendations, and building recommendation services at scale.
Topics Covered
- ✓Neural collaborative filtering
- ✓Session-based RNNs
- ✓Multi-armed bandits
- ✓A/B testing recommendations
- ✓Production architecture
Why This Series?
Real-World Focus
Learn from actual production experience at companies like Gartner, building recommendation systems that serve millions of users daily.
Complete Implementations
Every algorithm comes with working Python code using popular datasets like MovieLens. Copy, run, and modify for your own projects.
Production-Ready
Go beyond toy examples. Learn about scalability, caching, A/B testing, and deployment strategies used in real systems.
Progressive Learning
Start with fundamentals and progressively build to advanced techniques. Each part assumes knowledge from previous parts.
Ready to Start Building?
Begin with Part 1 to learn the foundations of recommender systems
Start Part 1: Foundations →