This chapter covers recommendation engine requirements: items to rank, users, freshness requirement, serving latency SLA.

This chapter covers key questions: collaborative vs content-based filtering, cold start problem, implicit vs explicit feedback.

This chapter covers simple popularity-based recommendations without personalization and why it fails for long-tail items.

This chapter covers two-tower neural model with batch offline training, feature store, and online serving with ANN index.

This chapter covers two approaches: matrix factorization vs deep learning models — trade-offs in accuracy and infrastructure cost.

This chapter covers how Netflix, Spotify, and YouTube generate personalized recommendations at user scale.

This chapter covers feature store for consistent training/serving features, A/B test infrastructure, cold start fallback strategy.

This chapter covers training-serving skew, feedback loop amplifying popularity bias, model staleness causing stale recs.