Specialized Topics
Recommendation Systems — The Algorithm Behind 'You Might Also Like'
Recommendation systems predict what users will like based on past behavior, powering billions of dollars in e-commerce and content revenue.
- Collaborative Filtering — finds patterns in user behavior to recommend items liked by similar users
- Content-Based Filtering — recommends items similar to what a user has already enjoyed using item features
- Matrix Factorization — decomposes sparse user-item matrices into dense latent factor representations
"Our head is a recommendation engine." — Jeff Bezos
Recommendation Systems — Complete Guide
Recommendation systems predict what users will like based on past behavior.
Mathematical Foundations
Cosine Similarity
Matrix Factorization Objective
Precision@K
NDCG@K
Types
Collaborative vs Content-Based Filtering
Collaborative Filtering
Matrix Factorization Diagram
Cold-Start Problem
Evaluation
Key Takeaways
What to Learn Next
-> Clustering Group similar users or items using K-Means, DBSCAN, and hierarchical methods.
-> Dimensionality Reduction Reduce sparse user-item matrices to dense representations with PCA and autoencoders.
-> Neural Networks Build deep learning models for neural collaborative filtering and representation learning.
-> Model Evaluation Master precision, recall, and ranking metrics for evaluating recommendation quality.
-> A/B Testing Design online experiments to measure the real-world impact of recommendation changes.
-> NLP Fundamentals Process item descriptions and user reviews with text mining for content-based recommendations.