article

Determining the informational, navigational, and transactional intent of Web queries

Authors:

Bernard J. Jansen,

Danielle L. Booth,

Amanda SpinkAuthors Info & Claims

Information Processing and Management: an International Journal, Volume 44, Issue 3

Pages 1251 - 1266

https://doi.org/10.1016/j.ipm.2007.07.015

Published: 01 May 2008 Publication History

Abstract

In this paper, we define and present a comprehensive classification of user intent for Web searching. The classification consists of three hierarchical levels of informational, navigational, and transactional intent. After deriving attributes of each, we then developed a software application that automatically classified queries using a Web search engine log of over a million and a half queries submitted by several hundred thousand users. Our findings show that more than 80% of Web queries are informational in nature, with about 10% each being navigational and transactional. In order to validate the accuracy of our algorithm, we manually coded 400 queries and compared the results from this manual classification to the results determined by the automated method. This comparison showed that the automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is vague or multi-faceted, pointing to the need for probabilistic classification. We discuss how search engines can use knowledge of user intent to provide more targeted and relevant results in Web searching.

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Cited By

Azzopardi LVan Der Sluis F(2024)Seeking Socially Responsible Consumers: Exploring the Intention-Search-Behaviour GapProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638324(153-164)Online publication date: 10-Mar-2024
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cover image Information Processing and Management: an International Journal

Information Processing and Management: an International Journal Volume 44, Issue 3

May, 2008

407 pages

ISSN:0306-4573

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Copyright © Elsevier Ltd © 2007.

Publisher

Pergamon Press, Inc.

United States

Publication History

Published: 01 May 2008

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Cited By

Azzopardi LVan Der Sluis F(2024)Seeking Socially Responsible Consumers: Exploring the Intention-Search-Behaviour GapProceedings of the 2024 Conference on Human Information Interaction and Retrieval10.1145/3627508.3638324(153-164)Online publication date: 10-Mar-2024
https://dl.acm.org/doi/10.1145/3627508.3638324
Fang QZhou ZBarbieri FLiu YNeves LNguyen DOberski DBos MDotsch RHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)General-Purpose User Modeling with Behavioral Logs: A Snapchat Case StudyProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657908(2431-2436)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657908
Corti LOltmans RJung JBalayn AWijsenbeek MYang J(2024)``It Is a Moving Process": Understanding the Evolution of Explainability Needs of Clinicians in Pulmonary MedicineProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642551(1-21)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642551
Massai L(2024)Evaluation of semantic relations impact in query expansion-based retrieval systemsKnowledge-Based Systems10.1016/j.knosys.2023.111183283:COnline publication date: 11-Jan-2024
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Zhao ZDou ZMao JWen JAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Generating Clarifying Questions with Web Search ResultsProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531981(234-244)Online publication date: 6-Jul-2022
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