Classical Machine Learning / recommenders-ranking

Recommender systems and ranking

Collaborative filtering, content-based retrieval, matrix factorization, cold start, ranking, and top-k metrics.

shellbackend needed later
Focus
  • candidate generation
  • learning to rank
  • implicit feedback
  • NDCG and MAP@K
Future feature idea

Candidate generation and ranking sandbox with NDCG, MAP@K, and Recall@K.

Self-check
  1. What is the core job of "Recommender systems and ranking"?
  2. Which common mistake would break a production implementation of this topic?
  3. Which inputs or limits must be validated before the interactive feature ships?
  4. What is the smallest test that proves the future implementation behaves correctly?
  5. When does this module really need backend compute, and when is a UI simulation enough?
Implementation notes
  • Start with one focused feature, not a full course inside one page.
  • All public inputs must be typed, bounded, and covered by reject-case tests.
  • If a model, dataset, or job is added, document source, license, limits, and fallback.
  • The interaction must explain the topic rather than serve as decoration.