Search Relevance Tuning: Elasticsearch + Business Logic
How I tuned search relevance: a business-logic boost layer on top of Elasticsearch, without replacing its relevance engine.
Problem
Onsite search often runs on Elasticsearch, whose relevance scoring is built around text-match quality and has no built-in concept of the business context that actually determines whether a listing is worth surfacing to a B2B buyer at a given moment — things like whether a product is still current and whether it's even available in the customer's market.
Approach
Rather than replacing Elasticsearch's relevance engine, I initiated and led the design of a boost layer on top of it: a calculation model that scores each product on business signals — availability and product lifecycle among them — and translates that into boosts and penalties applied on top of Elasticsearch's native relevance score. Elasticsearch still does what it's best at, matching and ranking by search intent, while the boost layer adds the business context it has no visibility into by default.
Outcome
Search results and product listings now reflect both what a customer is looking for and what is actually relevant to buy right now, without discarding Elasticsearch's underlying relevance model — a boost layer that can keep absorbing new business signals as they become relevant to weight in.
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