Implement collaborative filtering (matrix factorization, neighborhood methods) and content-based recommendation. Design hybrid systems, handle cold start, and evaluate with precision, recall, and diversity metrics.
20 lessons
2 h
advanced
What you'll learn
Define recommendation systems and their role in information retrieval
Identify major types of recommendation approaches
Explain the principle of collaborative filtering
Differentiate between user-based and item-based approaches
Implement user-based and item-based neighborhood methods
Evaluate the strengths and weaknesses of neighborhood approaches
Finishing the course
80 knowledge-check questions along the way, so you know the ideas stuck before you move on.
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