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Understanding user goals in web search
Abstract
Previous work on understanding user web search behavior has focused on how people search and what they are searching for, but not why they are searching. In this paper, we describe a framework for understanding the underlying goals of user searches, and our experience in using the framework to manually classify queries from a web search engine. Our analysis suggests that so-called navigational" searches are less prevalent than generally believed while a previously unexplored "resource-seeking" goal may account for a large fraction of web searches. We also illustrate how this knowledge of user search goals might be used to improve future web search engines.
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“Classic web search work proposes query intent taxonomies including Broder's influential three-way split into informational, navigational, and transactional intents (Broder, 2002) and subsequent refinements (Rose & Levinson, 2004;Jansen et al., 2008;Jansen & Booth, 2010;Cambazoglu et al., 2021).”
From Understanding Usage and Engagement in AI-Powered Scientific Research Tools: The Asta Interaction Dataset (2026) · cites this paper as Rose & Levinson, 2004
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Research works printed but not linked: 9
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- Bates, 1989, J. The Design of Browsing and Berrypicking Techniques for the Online Search Interface. Online Review, 13, 407
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- 10.1145/792550.792552 · DOI · Paper page
- 10.1002/1097-4571(2000)9999:9999%3C::AID-ASI1607%3E3.3.CO;2-6 · DOI
- 10.5555/1124169.1124174 · DOI
- 10.1145/331403.331405 · DOI
- 10.1109/2.989940 · DOI
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Athukorala, K., Głowacka, D., Jacucci, G., Oulasvirta, A., & Vreeken, J. (2016) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.23617 · Cited by 150
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Session Viewer: Visual Exploratory Analysis of Web Session Logs
Lam, H., Russell, D., Tang, D., & Munzner, T. (2007) · 2007 IEEE Symposium on Visual Analytics Science and Technology · doi:10.1109/vast.2007.4389008 · Cited by 62
ExpertQA: Expert-Curated Questions and Attributed Answers
Malaviya, C., Lee, S., Chen, S., Sieber, E., Yatskar, M., & Roth, D. (2024) · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · doi:10.18653/v1/2024.naacl-long.167 · Cited by 28
Summary attributes and perceived search quality
Rose, D. E., Orr, D., & Kantamneni, R. G. P. (2007) · Proceedings of the 16th international conference on World Wide Web · doi:10.1145/1242572.1242765 · Cited by 22
Self-citationYu, J. E., Parde, N., & Chattopadhyay, D. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581447 · Cited by 22
Haddad, D., Bareket, D., Chang, J. C., DeYoung, J., Hwang, J. D., Katz, U., Polak, M., Suh, S., Surana, H., Tiktinsky, A., et al. (2026) · arXiv · doi:10.48550/arxiv.2602.23335 · Cited by 4
Metadata
- Authors
- Rose, D. E., & Levinson, D.
- Affiliations
- Yahoo! Inc., Sunnyvale, CA
- Yahoo! Inc., Calgary, AB Canada
- Venue
- Proceedings of the 13th international conference on World Wide Web
- Publisher
- ACM
- Published
- 2004-05-17
- Pages
- 13-19
- Cited by
- 563
- References
- 0
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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