Improving search engines by query clustering. Journal of the American Society for Information Science and Technology, 58(12):1793-1804, 2007. doi abstract bibtex In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.
@article{10.1002/asi.20627,
abstract = "In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.",
number = "12",
year = "2007",
title = "Improving search engines by query clustering",
volume = "58",
pages = "1793-1804",
doi = "10.1002/asi.20627",
journal = "Journal of the American Society for Information Science and Technology"
}
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