poster

Predicting when browsing context is relevant to search

Authors:

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

Pages 841 - 842

https://doi.org/10.1145/1390334.1390532

Published: 20 July 2008 Publication History

Get Access

Abstract

We investigate a representative case of sudden information need change of Web users. By analyzing search engine query logs, we show that the majority of queries submitted by users after browsing documents in the news domain are related to the most recently browsed document. We investigate ways of identifying whether a query is a good candidate for contextualization conditioned on the most recently browsed document by a user. We build a successful classifier for this task, which achieves 96% precision at 90% recall.

References

[1]

P. A. Chirita, C. S. Firan, and W. Nejdl. Personalized query expansion for the web. In Proceedings of SIGIR'07, pages 7--14, 2007.

Digital Library

Google Scholar

[2]

C. Cool and A. Spink. Issues of context in information retrieval: an introduction to the special issue. Information Processing and Management, 38(5):605--611, 2002.

Digital Library

Google Scholar

[3]

P. Ingwersen and K. Järvelina. The Turn: Integration of Information Seeking and Retrieval in Context. Springer, 2005.

Digital Library

Google Scholar

[4]

F. Qiu and J. Cho. Automatic identification of user interest for personalized search. In Proceedings WWW'06, pages 727--736, 2006.

Digital Library

Google Scholar

[5]

X. Shen and C. Zhai. Exploiting query history for document ranking in interactive information retrieval. In Proc. of SIGIR'03, pages 377--378, 2003.

Digital Library

Google Scholar

[6]

K. Sugiyama, K. Hatano, and M. Yoshikawa. Adaptive web search based on user profile constructed without any effort from users. In Proceedings of WWW'04, pages 675--684, 2004.

Digital Library

Google Scholar

[7]

J. Teevan, S. T. Dumais, and E. Horvitz. Characterizing the value of personalizing search. In Proceedings of SIGIR'07, pages 757--758, 2007.

Digital Library

Google Scholar

Cited By

View all

Sheetrit EFyodorov YRaiber FKurland OHasibi FFang YAizawa A(2021)Recommending Search Queries in Documents Using Inter N-Gram SimilaritiesProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472252(211-220)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3471158.3472252
Gasparetti F(2017)Modeling user interests from web browsing activitiesData Mining and Knowledge Discovery10.1007/s10618-016-0482-x31:2(502-547)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10618-016-0482-x
Kong WLi RLuo JZhang AChang YAllan JBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Predicting Search Intent Based on Pre-Search ContextProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767757(503-512)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767757
Show More Cited By

Index Terms

Predicting when browsing context is relevant to search
1. Information systems
  1. Information retrieval
    1. Information retrieval query processing

Recommendations

Using the current browsing context to improve search relevance
CIKM '08: Proceedings of the 17th ACM conference on Information and knowledge management

In this paper, we investigate the problem of improving the relevance of a Web search engine by adapting it to the dynamic needs of the user. We examine a representative case of sudden information need change, namely the exposure of the user to news ...
Actively predicting diverse search intent from user browsing behaviors
WWW '10: Proceedings of the 19th international conference on World wide web

This paper is concerned with actively predicting search intent from user browsing behavior data. In recent years, great attention has been paid to predicting user search intent. However, the prediction was mostly passive because it was performed only ...
Discovering search engine related queries using association rules

This work presents a method for online generation of query related suggestions for a Web search engine. The method uses association rules to extract related queries from the log of sbumitted queries to the search engine. Experimental results were ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval

July 2008

934 pages

ISBN:9781605581644

DOI:10.1145/1390334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Mun-Kew Leong
National Library Board, Singapore
,
Program Chairs:
Syung Hyon Myaeng
Information and Communications University, Korea
,
Douglas W. Oard
University of Maryland, College Park, USA
,
Fabrizio Sebastiani
Consiglio Nazionale delle Ricerche, Italy

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

SIGIR '08

Sponsor:

SIGIR '08: The 31st Annual International ACM SIGIR Conference

July 20 - 24, 2008

Singapore, Singapore

Acceptance Rates

Overall Acceptance Rate 792 of 3,983 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

9
Total Citations
View Citations
351
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 16 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sheetrit EFyodorov YRaiber FKurland OHasibi FFang YAizawa A(2021)Recommending Search Queries in Documents Using Inter N-Gram SimilaritiesProceedings of the 2021 ACM SIGIR International Conference on Theory of Information Retrieval10.1145/3471158.3472252(211-220)Online publication date: 11-Jul-2021
https://dl.acm.org/doi/10.1145/3471158.3472252
Gasparetti F(2017)Modeling user interests from web browsing activitiesData Mining and Knowledge Discovery10.1007/s10618-016-0482-x31:2(502-547)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1007/s10618-016-0482-x
Kong WLi RLuo JZhang AChang YAllan JBaeza-Yates RLalmas MMoffat ARibeiro-Neto B(2015)Predicting Search Intent Based on Pre-Search ContextProceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/2766462.2767757(503-512)Online publication date: 9-Aug-2015
https://dl.acm.org/doi/10.1145/2766462.2767757
Cucerzan SAlonso OKamps JKarlgren J(2014)Linking to Web Knowledge Bases and Applications to Web SearchProceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval10.1145/2663712.2666199(3-3)Online publication date: 7-Nov-2014
https://dl.acm.org/doi/10.1145/2663712.2666199
Ustinovskiy YSerdyukov PHe QIyengar ANejdl WPei JRastogi R(2013)Personalization of web-search using short-term browsing contextProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2505679(1979-1988)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2505679
Liebling DBennett PWhite RHersh WCallan JMaarek YSanderson M(2012)Anticipatory searchProceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval10.1145/2348283.2348456(1035-1036)Online publication date: 12-Aug-2012
https://dl.acm.org/doi/10.1145/2348283.2348456
Watanabe NOkamoto MKikuchi MIida TSasaki KHoriuchi KHattori M(2011)Designing mobile search interface with query term extractionProceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part III10.5555/2041420.2041430(67-76)Online publication date: 12-Sep-2011
https://dl.acm.org/doi/10.5555/2041420.2041430
Okamoto MWatanabe NKikuchi MIida TSasaki KHoriuchi KYamasaki TOmura SHattori MAngelides MLambrinos LRohs MRukzio E(2010)First query term extraction from current webpage for mobile applicationsProceedings of the 9th International Conference on Mobile and Ubiquitous Multimedia10.1145/1899475.1899494(1-9)Online publication date: 1-Dec-2010
https://dl.acm.org/doi/10.1145/1899475.1899494
Rahurkar MCucerzan SShanahan JAmer-Yahia SManolescu IZhang YEvans DKolcz AChoi KChowdury A(2008)Using the current browsing context to improve search relevanceProceedings of the 17th ACM conference on Information and knowledge management10.1145/1458082.1458351(1493-1494)Online publication date: 26-Oct-2008
https://dl.acm.org/doi/10.1145/1458082.1458351

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Using the current browsing context to improve search relevance

Actively predicting diverse search intent from user browsing behaviors

Discovering search engine related queries using association rules