Explain why recommendation systems commonly split retrieval, scoring, and re-ranking.
The move: do not ask one model to do every job. Retrieval asks for breadth Retrieval is the wide net. It should be fast, tolerant, and intentionally redundant. If the right item never enters the candidate pool, the ranker cannot save it. Retrieval quality is about recall under latency and eligibility constraints. Ranking asks for comparability Ranking turns a mixed candidate pool into a single ordered list. This stage can use richer features: user history, item metadata, session context, predicted satisfaction, and calibrated business signals. It is where the system decides which candidate is more valuable for this user right…
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