Faceted search

Faceted search augments lexical search with a faceted navigation system, allowing users to narrow results by applying filters based on a faceted classification of the items.[1] It is a parametric search technique.[2] A faceted classification system classifies each information element along multiple explicit dimensions, facets, enabling the classifications to be accessed and ordered in multiple ways rather than in a single, pre-determined, taxonomic order.[1]

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.[3][4][5][6] The most well-known of these efforts was the Flamenco research project at University of California, Berkeley led by Marti Hearst.[7] 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.[8]

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.[9]

As of 2014, among the 50 largest US-based online retailers, 40% had implemented faceted search.[10] 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.[11]

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[12] at the Pennsylvania State University allows faceted search for academic documents and continues to expand into other facets such as table search.

See also

References
  1. Tunkelang, Daniel (2009). "Faceted Search". Synthesis Lectures on Information Concepts, Retrieval, and Services. Morgan & Claypool. 1: 1–80. doi:10.2200/S00190ED1V01Y200904ICR005. S2CID 2430723.
  2. "Parametric Search, Faceted Search, and Taxonomies - New Idea Engineering". www.ideaeng.com. Retrieved 22 July 2022.
  3. Shneiderman, Ben (1994). "Dynamic queries for visual information seeking". IEEE Software. 11 (6): 70–77. doi:10.1109/52.329404. hdl:1903/388. S2CID 8021243.
  4. Pollitt, Steven; Smith, Martin; Treglown, Mark; Braekevelt, Patrick (1996). "View-based searching systems—progress towards effective disintermediation". Online Information 96 Proceedings: 433–441.
  5. Yee, Ka-Ping; Swearingen, Kirsten; Li, Kevin; Hearst, Marti (2003-04-05). "Faceted metadata for image search and browsing". Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI '03. New York, NY, USA: Association for Computing Machinery: 401–408. doi:10.1145/642611.642681. ISBN 978-1-58113-630-2. S2CID 367518.
  6. Hill, Gary Marchionini; Interaction Design Laboratory, University of North Carolina at Chapel Hill Ben Brunk; Interaction Design Laboratory, University of North Carolina at Chapel (2003-01-03). Towards a General Relation Browser: A GUI for Information Architects. Texas Digital Library. OCLC 751844113.{{cite book}}: CS1 maint: multiple names: authors list (link)
  7. Flamenco project
  8. "SIGIR'2006 Workshop on Faceted Search - Call for Participation". Facetedsearch.googlepages.com. 2006-08-10. Retrieved 2019-03-19.
  9. 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.'
  10. Smashing Magazine: The Current State of E-Commerce Search Retrieved on 2014-08-27.
  11. "Major classification systems : the Dewey Centennial". 2007-08-01. Retrieved 2019-03-19.
  12. CiteSeerX. Citeseerx.ist.psu.edu. Retrieved on 2013-07-21.
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