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Semantic similarity between search engine queries using temporal correlation

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Semantic similarity between search engine queries using temporal correlation
We investigate the idea of finding semantically related search engine queries based on their temporal correlation; in other words, we infer that two queries are related if their popularities behave similarly over time. To this end, we first define a new measure of the temporal correlation of two queries based on the correlation coefficient of their frequency functions. We then conduct extensive experiments using our measure on two massive query streams from the MSN search engine, revealing that this technique can discover a wide range of semantically similar queries. Finally, we develop a method of efficiently finding the highest correlated queries for a given input query using far less space and time than the naive approach, making real-time implementation possible. Categories and Subject Descriptors H.3.3 [Information Systems]: Information Storage and Retrieval--Information Search and Retrieval General Terms Algorithms, Experimentation Keywords search engines, query stream analysis,...
Steve Chien, Nicole Immorlica
Added 22 Nov 2009
Updated 22 Nov 2009
Type Conference
Year 2005
Where WWW
Authors Steve Chien, Nicole Immorlica
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