Study collaborative filtering through preference signals, compact similarity coding, fairness-aware recommendation, and factorization models.
Explore recommender systems from collaborative filtering and alternative preference signals to bit-level similarity coding, fairness-aware recommendation, and factorization machines. The sequence emphasizes how feedback is collected, what user-item interactions encode, and how ranking quality and fairness depend on evaluation design. Treat each method as a research proposal to test against transparent baselines, not a universal solution. The English-audio videos pair with original notes, not copied transcripts.
Distinguish explicit and implicit preference signals and identify their collection biases. Explain how similarity coding and factorization models represent user-item relationships. Design a recommendation evaluation that reports ranking quality, coverage, and fairness-sensitive effects.
Familiarity with supervised learning, matrix operations, and evaluation metrics is recommended. Prior knowledge of recommender systems is helpful but not required; comfort with user-item tables and ranking concepts will support the course.
Experienced learners who want to deepen and apply advanced AI skills
People who learn best through examples, guided lessons, and hands-on practice
Professionals, creators, and independent builders looking for a repeatable workflow
Lesson 1 is free
4 lessons · Advanced · Full course.
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