Allow low certainty to lead to more cautious behavior.
Confidence indicates how much confidence the system has in an interpretation or match. It is not an absolute truth. Well-used confidence determines when the search engine is allowed to trade automatically, when multiple options need to remain and when a secure fallback is needed.
What a confidence score means and not
A score may indicate that a term is probably a brand, that a correction is plausible or that a semantic match seems strong. The scale and meaning, however, differ per component. A value of 0.8 is not automatically “80 percent chance that this product is good”.
Therefore, use scores only when they are calibrated to real examples and linked to a concrete decision. The relevant question is: at which score is automatic filtering safe, when do we show a suggestion and when do we do nothing?
Confidence by decision point
- Query interpretation: is clear what product type, brand or attribute is?
- Spelling Correction: is the proposed correction more likely than the original term?
- Entity coupling: does the recognized value fit an existing catalog field?
- Semantic match: is the meaning agreement strong enough to add a candidate?
- Constraint: is there enough security to exclude products hard?
Every decision point may require a different threshold. An error in a suggestion is less profound than a mistake that makes half the assortment invisible.
Actions by security level
With high security, the system can apply a well-known brand value or size directly. In case of medium certainty, it can use the interpretation as a ranking boost without excluding products. With low certainty, it can keep multiple routes open, show a clarification or lead lexical search.
This gradation prevents all-or-nothing behavior. An uncertain signal can still deliver value, but does not get more decision-making power than is justified.
Calibrate thresholds
Start with a labeled set of real search questions. Compare the scores with human assessments and see where mistakes arise. Pay separate attention to different query types, languages and categories; a global threshold can hide strong and weak segments.
Calibration must be re-checked after model, product data, or configuration changes. A score distribution can shift while the same numerical threshold remains.
Practical example
A customer types “Samsung tv 55 inch”. The correction to Samsung can be highly plausible, 55 inches is a recognizable size and TV a product type. The system can use this combination directly. With an unknown short term that can be both brand and model, a broad automatic correction is riskier.
In that second case, the search engine can retain exact word results, show possible categories and use the semantic interpretation only as an additional signal.
No false precision
Show internal not only a score, but also the source, version, threshold and chosen action. A high number without context does not make management better. Explainability helps a merchandiser or search specialist understand why a query was treated differently.
Customers usually do not require a technical percentage. A clear suggestion, filter chip or explanation of an alternative is more understandable.
Monitors in production
Compare low-confidence queries with zero-result searches, reformulations, deleted filters and conversion. Also pay attention to categories where the system is structurally too certain and makes wrong exclusions.
Findoviq uses confidence to control guardrails and fallbacks. This allows AI to have more space where it demonstrably helps and the behavior remains reluctant when the interpretation is not strong enough.
Discuss your search questions
Do you want to know how this approach fits your assortment, product data and customer behavior? Together, we look at which query types have priority and where exact, semantic and business signals need to complement each other.