Compare book, movie, and playlist recommendation prototypes by data assumptions, product goals, privacy choices, and evaluation evidence.
Use distinct recommendation settings to reason about interaction-based and content-based prototypes, then assess what a compact demonstration can establish. Define feedback events and ranking goals, inspect item attributes and exposure bias, and plan consent, diversity, cold-start, and user-control checks. Assignments use invented cases or permitted catalog metadata; they do not claim that a demo predicts universal taste or proves product impact.
Define recommendation objectives, users, items, context, and feedback events for distinct product settings. Compare interaction-based and content-based approaches while identifying sparsity, exposure, privacy, and metadata limitations. Design evaluations and user controls for baselines, coverage, diversity, and evidence beyond a successful demo.
Basic familiarity with tables, product metrics, and introductory ML vocabulary is recommended. No programming is required. Use only data you are permitted to process, avoid identifiable listening or viewing histories, and do not infer sensitive traits or universal preferences from a recommendation example.
Learners with basic familiarity who are ready to build practical, independent 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 · Intermediate · Full course.
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