Abstract
We present a method to help a user redefine a query suggesting a list of similar queries. The method proposed is based on click-through data were sets of similar queries could be identified. Scientific literature shows that similar queries are useful for the identification of different information needs behind a query. Unlike most previous work, in this paper we are focused on the discovery of better queries rather than related queries. We will show with experiments over real data that the identification of better queries is useful for query disambiguation and query specialization.
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© 2006 International Federation for Information Processing
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Dupret, G., Mendoza, M. (2006). Automatic Query Recommendation using Click-Through Data. In: Debenham, J. (eds) Professional Practice in Artificial Intelligence. IFIP WCC TC12 2006. IFIP International Federation for Information Processing, vol 218. Springer, Boston, MA . https://doi.org/10.1007/978-0-387-34749-3_32
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DOI: https://doi.org/10.1007/978-0-387-34749-3_32
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