Real life, real users, and real needs: a study and analysis of user queries on the web
Information Processing and Management: an International Journal
ACM SIGIR Forum
Understanding user goals in web search
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Automatic identification of user goals in Web search
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How are we searching the world wide web?: a comparison of nine search engine transaction logs
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The intention behind web queries
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
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A large scale study of European mobile search behaviour
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Understanding the intent behind mobile information needs
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Liquid query: multi-domain exploratory search on the web
Proceedings of the 19th international conference on World wide web
Ready to buy or just browsing?: detecting web searcher goals from interaction data
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Query intent prediction and recommendation
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Search Computing
Chapter 13: liquid queries and liquid results in search computing
Search Computing
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A feature-free search query classification approach using semantic distance
Expert Systems with Applications: An International Journal
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Mining entity types from query logs via user intent modeling
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Journal of the American Society for Information Science and Technology
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Proceedings of the 22nd international conference on World Wide Web
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Journal of Biomedical Informatics
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Expert Systems with Applications: An International Journal
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Determining the user intent of Web searches is a difficult problem due to the sparse data available concerning the searcher. In this paper, we examine a method to determine the user intent underlying Web search engine queries. We qualitatively analyze samples of queries from seven transaction logs from three different Web search engines containing more than five million queries. From this analysis, we identified characteristics of user queries based on three broad classifications of user intent. The classifications of informational, navigational, and transactional represent the type of content destination the searcher desired as expressed by their query. We implemented our classification algorithm and automatically classified a separate Web search engine transaction log of over a million 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 classification to the results from our algorithm. This comparison showed that our automatic classification has an accuracy of 74%. Of the remaining 25% of the queries, the user intent is generally vague or multi-faceted, pointing to the need to for probabilistic classification. We illustrate how knowledge of searcher intent might be used to enhance future Web search engines.