Mining Query Logs: Turning Search Usage Data into Knowledge

IF 8.3 2区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS Foundations and Trends in Information Retrieval Pub Date : 2010-01-01 DOI:10.1561/1500000013
F. Silvestri
{"title":"Mining Query Logs: Turning Search Usage Data into Knowledge","authors":"F. Silvestri","doi":"10.1561/1500000013","DOIUrl":null,"url":null,"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.","PeriodicalId":48829,"journal":{"name":"Foundations and Trends in Information Retrieval","volume":"1 1","pages":"1-174"},"PeriodicalIF":8.3000,"publicationDate":"2010-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"200","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations and Trends in Information Retrieval","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1561/1500000013","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 200

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.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
挖掘查询日志:将搜索使用数据转化为知识
Web搜索引擎在其日志中保存了自其开始运行以来的用户信息。这些信息通常有多种用途。本调查的主要重点是通过展示查询挖掘的基础,并通过分析用于从这个(潜在的)无限信息源中提取有用知识的基本算法和技术,来介绍查询挖掘的学科。通过分析查询日志挖掘的流行应用程序及其对用户体验的影响,我们展示了搜索应用程序如何从这种分析中受益。我们通过简要地介绍该领域中一些最具挑战性的当前开放问题来结束本文。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Foundations and Trends in Information Retrieval
Foundations and Trends in Information Retrieval COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
39.10
自引率
0.00%
发文量
3
期刊介绍: The surge in research across all domains in the past decade has resulted in a plethora of new publications, causing an exponential growth in published research. Navigating through this extensive literature and staying current has become a time-consuming challenge. While electronic publishing provides instant access to more articles than ever, discerning the essential ones for a comprehensive understanding of any topic remains an issue. To tackle this, Foundations and Trends® in Information Retrieval - FnTIR - addresses the problem by publishing high-quality survey and tutorial monographs in the field. Each issue of Foundations and Trends® in Information Retrieval - FnT IR features a 50-100 page monograph authored by research leaders, covering tutorial subjects, research retrospectives, and survey papers that provide state-of-the-art reviews within the scope of the journal.
期刊最新文献
Multi-hop Question Answering User Simulation for Evaluating Information Access Systems Conversational Information Seeking Perspectives of Neurodiverse Participants in Interactive Information Retrieval Efficient and Effective Tree-based and Neural Learning to Rank
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1