Recommendation, Ranking, and Personalization Questions

Systems that select and order items for users. Covers candidate generation and ranking (two-stage retrieval, embedding-based retrieval with two-tower models and approximate nearest-neighbour candidate lookup, multi-stage scoring and cross-encoder re-ranking), collaborative and content-based filtering, matrix factorization (alternating least squares, Bayesian personalized ranking), sequence-aware and graph-based recommenders, cold start, learning-to-rank and negative sampling, turning noisy implicit feedback into labels, position, exposure and popularity bias and feedback loops, diversity, fairness-aware exposure and multi-objective re-ranking, counterfactual (off-policy) evaluation of recommenders (inverse propensity scoring, doubly robust estimators), and exploration with multi-armed and contextual bandits (epsilon-greedy, upper-confidence-bound and Thompson sampling, LinUCB, regret, non-stationarity, constraints, rollout, and when a bandit beats a fixed split or full reinforcement learning). Includes end-to-end recommender and personalized feed design. Focuses on the modeling and evaluation patterns specific to recommendation and ranking at scale. General A/B test design, generic ranking-metric definitions and ML serving infrastructure are covered elsewhere.