What strategies would you use to minimize recommendation system latency?

Example Answer

I would profile the request path before choosing an optimization: feature reads, candidate retrieval, ranking, filtering and network overhead. Precomputed embeddings, approximate retrieval or smaller rankers can help, but their quality and freshness trade-offs need measurement. Cache only under keys that preserve personalization, eligibility and model version. Set a request deadline with a safe fallback, and measure tail latency on realistic load rather than inventing a compression or speedup result.

Make it your own

Illustrative benchmark sheet: p50/p95/p99 latency, candidate recall, ranking quality, cache-hit rate, stale-item rate, peak memory and fallback frequency.

Why this works

A faster neural kernel may leave the real database bottleneck unchanged. Follow-up: how would you diagnose a p99 regression when average latency improves?

Sources: Google recommendation architecture, Google recommendation re-ranking.