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Relevance fundamentals

Retrieval finds the options. Ranking finds the order.

Why a useful recommendation workflow needs both a candidate set and a clear way to order it.

RankNexa · The relevance notebook

Start with the candidate set

A catalog can contain more items than a product experience can show. Candidate retrieval narrows that catalog to a useful set. Content similarity, collaborative signals, popularity, and semantic retrieval can each contribute, depending on the available data.

Rank for the actual context

Ranking scores and orders the candidates for a particular user, session, or placement. Recent actions and catalog attributes can matter alongside historical interests. Re-ranking can also work on results returned by an existing search system.

Apply constraints deliberately

Availability, eligibility, diversity, and duplicate suppression are distinct from a learned relevance score. Make those constraints explicit so a technically relevant result still fits the product experience.

Evaluate the whole journey

A ranking change should be assessed against a defined objective and guardrails. Item coverage and segment behavior can add context to aggregate engagement. Choose a baseline before interpreting improvement.

Let’s find your starting point

Great catalogs.
Even better discovery.

Bring your product, your catalog, and your biggest relevance question. Let’s talk about what a better experience could look like.

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