Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information. We show how search applications may benefit from this kind of analysis by analyzing popular applications of query log mining and their influence on user experience. We conclude the paper by, briefly, presenting some of the most challenging current open problems in this field.
%0 Journal Article
%1 silvestri2010mining
%A Silvestri, Fabrizio
%C Hanover, MA, USA
%D 2010
%I Now Publishers Inc.
%J Found. Trends Inf. Retr.
%K analysis implicit-feedback overview query-log web-mining
%P 1--174
%R 10.1561/1500000013
%T Mining Query Logs: Turning Search Usage Data into Knowledge
%U http://dx.doi.org/10.1561/1500000013
%V 4
%X Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information. We show how search applications may benefit from this kind of analysis by analyzing popular applications of query log mining and their influence on user experience. We conclude the paper by, briefly, presenting some of the most challenging current open problems in this field.
@article{silvestri2010mining,
abstract = {Web search engines have stored in their logs information about users since they started to operate. This information often serves many purposes. The primary focus of this survey is on introducing to the discipline of query mining by showing its foundations and by analyzing the basic algorithms and techniques that are used to extract useful knowledge from this (potentially) infinite source of information. We show how search applications may benefit from this kind of analysis by analyzing popular applications of query log mining and their influence on user experience. We conclude the paper by, briefly, presenting some of the most challenging current open problems in this field.},
acmid = {1795387},
added-at = {2011-07-25T17:02:46.000+0200},
address = {Hanover, MA, USA},
author = {Silvestri, Fabrizio},
biburl = {https://www.bibsonomy.org/bibtex/2a21939379d7413242f8947f781a66a9f/beate},
description = {Mining Query Logs},
doi = {10.1561/1500000013},
interhash = {6375bb35822eba076e4f9e79b66908c2},
intrahash = {a21939379d7413242f8947f781a66a9f},
issn = {1554-0669},
issue = {1\&\#8212;2},
journal = {Found. Trends Inf. Retr.},
keywords = {analysis implicit-feedback overview query-log web-mining},
month = {January},
numpages = {174},
pages = {1--174},
publisher = {Now Publishers Inc.},
timestamp = {2011-07-25T17:02:46.000+0200},
title = {Mining Query Logs: Turning Search Usage Data into Knowledge},
url = {http://dx.doi.org/10.1561/1500000013},
volume = 4,
year = 2010
}