Faceted search is a technique that involves augmenting traditional search techniques with a faceted navigation system, allowing users to narrow down search results by applying multiple filters based on faceted classification of the items. It is sometimes referred to as a parametric search technique. A faceted classification system classifies each information element along multiple explicit dimensions, called facets, enabling the classifications to be accessed and ordered in multiple ways rather than in a single, pre-determined, taxonomic order.

Facets correspond to properties of the information elements. They are often derived by analysis of the text of an item using entity extraction techniques or from pre-existing fields in a database such as author, descriptor, language, and format. Thus, existing web-pages, product descriptions or online collections of articles can be augmented with navigational facets.

Faceted search interfaces were first developed in the academic world by Ben Shneiderman, Steven Pollitt, Marti Hearst, and Gary Marchionini in the 1990s and 2000s. The most well-known of these efforts was the Flamenco research project at University of California, Berkeley led by Marti Hearst. Concurrently, there was development of commercial faceted search systems, notably Endeca and Spotfire.

Within the academic community, faceted search has attracted interest primarily among library and information science researchers, and to some extent among computer science researchers specializing in information retrieval.