A practical guide to designing, building, and evaluating recommendation systems. This course covers the fundamental algorithms, including content-based and collaborative filtering, and advances to model-based techniques like matrix factorization. You will learn to process user-item interaction data, generate personalized recommendations, and measure the performance of your systems using industry-standard metrics. The focus is on a hands-on approach, enabling you to construct functional recommenders from the ground up.
Prerequisites Python and ML fundamentals
Level:
Implement Content-Based Filtering
Construct a recommendation system that suggests items based on their properties and user profiles.
Build Collaborative Filtering Models
Develop neighborhood-based recommenders that leverage user-item interaction patterns.
Apply Matrix Factorization
Use techniques like Singular Value Decomposition (SVD) to create powerful model-based recommenders.
Evaluate Recommender Performance
Measure and compare the effectiveness of different recommendation models using offline metrics like precision, recall, and NDCG.
Construct a Hybrid System
Combine multiple recommendation strategies to improve performance and address common issues like the cold-start problem.