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.
This article contains instructions, advice, or how-to content. (August 2022)
If a website is dealing with faceted navigation issues, there are several ways to fix it:
- Using Canonical tag
- Configuring URL Parameters report in Google Search Central
- Fix Crawl via robots.txt file.
- Nofollow links or remove internal links that cause faceted navigation.
Mass market use
Faceted search has become a popular technique in commercial search applications, particularly for online retailers and libraries. An increasing number of enterprise search vendors provide software for implementing faceted search applications.
Online retail catalogs pioneered the earliest applications of faceted search, reflecting both the faceted nature of product data (most products have a type, brand, price, etc.) and the ready availability of the data in retailers' existing information-systems. In the early 2000s retailers started using faceted search, in part due to published studies that evaluated user search experience on popular sites.
A 2014 benchmark of 50 of the largest US based online retailers revealed that despite the benefits of faceted search, only 40% of the sites had implemented it. Examples include the filtering options that appear in the left column on amazon.com or Google Shopping after a keyword search has been performed.
Libraries and information science
In 1933, the noted librarian Ranganathan proposed a faceted classification system for library materials, known as colon classification. In the pre-computer era, he did not succeed in replacing the pre-coordinated Dewey Decimal Classification system.
Modern online library catalogs, also known as online public access catalogs (OPAC), have increasingly adopted faceted search interfaces. Noted examples include the North Carolina State University library catalog (part of the Triangle Research Libraries Network) and the OCLC Open WorldCat system. The CiteSeerX project at the Pennsylvania State University allows faceted search for academic documents and continues to expand into other facets such as table search.
- Enterprise search
- Exploratory search
- Faceted classification
- Human–computer information retrieval
- Information extraction
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-  Flamenco project
- "SIGIR'2006 Workshop on Faceted Search - Call for Participation". Facetedsearch.googlepages.com. 2006-08-10. Retrieved 2019-03-19.
- Nielsen Norman Group. "The State of Ecommerce Search". Nielsen Norman Group. Retrieved 2021-12-13.
In our first study on ease of search experience for users, we concluded that '27% of task failures were a result of not being able to locate a suitable item on the site, even though all of our tasks were designed so there was always at least one item available.'
- Smashing Magazine: The Current State of E-Commerce Search Retrieved on 2014-08-27.
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- CiteSeerX. Citeseerx.ist.psu.edu. Retrieved on 2013-07-21.