Identifying queries in the wild, wild web

Authors:
Jingjing Liu;Chang Liu;Jun Zhang;Ralf Bierig;Michael J. Cole
Affiliations:
Rutgers University, New Brunswick, NJ, USA;Rutgers University, New Brunswick, NJ, USA;Rutgers University, New Brunswick, NJ, USA;Rutgers University, New Brunswick, NJ, USA;Rutgers University, New Brunswick, NJ, USA
Venue:
Proceedings of the third symposium on Information interaction in context
Year:
2010

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Abstract

Identifying user querying behavior is an important problem for information seeking and retrieval research. Query-related studies typically rely on server-side logs taken from a single search engine, but a comprehensive view of user querying behaviors requires analysis of data collected from the client-side for unrestricted searches. We developed three methods to identify querying behaviors and tested them on client-side logs collected in a lab experiment for realistic tasks and unrestricted searches on the entire Web. Results show that the best method was able to identify 97% of queries issued, with a precision of 92%. Although based on a relatively small number of search episodes, our methods, perhaps with minimal modifications, should be adequate for identification of queries in logs of unconstrained Web search.