System Design Article

Recommendation Systems Architecture

Difficulty: Hard

A recommendation system at scale is a multi-stage funnel: candidate generation narrows millions of items to a few thousand, light ranking trims to a few hundred, heavy ranking scores those, and a re-ranking stage applies business and policy constraints. Each stage has a different latency budget, a different model, and a different operational profile. This lesson covers the canonical architecture (retrieval + ranking + re-ranking), the core algorithmic families (collaborative filtering, content-based, two-tower neural retrieval, sequential models), the embedding store and vector ANN serving stack, the cold-start problem, ranking objectives and the metrics that measure them, and the rollout / monitoring discipline that keeps the system honest. The goal is to leave you able to design the recommendation system for any consumer product and defend every layer's choices.