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PQC: personalized query classification

Authors:

Evan Wei Xiang,

Zheng ChenAuthors Info & Claims

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

Pages 1217 - 1226

https://doi.org/10.1145/1645953.1646108

Published: 02 November 2009 Publication History

Abstract

Query classification (QC) is a task that aims to classify Web queries into topical categories. Since queries are usually short in length and ambiguous, the same query may need to be classified to different categories according to different people's perspectives. In this paper, we propose the Personalized Query Classification (PQC) task and develop an algorithm based on user preference learning as a solution. Users' preferences that are hidden in clickthrough logs are quite helpful for search engines to improve their understandings of users' queries. We propose to connect query classification with users' preference learning from clickthrough logs for PQC. To tackle the sparseness problem in clickthrough logs, we propose a collaborative ranking model to leverage similar users' information. Experiments on a real world clickthrough log data show that our proposed PQC algorithm can gain significant improvement compared with general QC as well as natural baselines. Our method can be applied to a wide range of applications including personalized search and online advertising.

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Cited By

Zhang HSong WLiu LDu CZhao X(2017)Query Classification Using Convolutional Neural Networks2017 10th International Symposium on Computational Intelligence and Design (ISCID)10.1109/ISCID.2017.212(441-444)Online publication date: Dec-2017
https://doi.org/10.1109/ISCID.2017.212
Rashidghalam HMahmoudi F(2015)Web query classification using improved visiting probability algorithm and babelnet semantic graph2015 AI & Robotics (IRANOPEN)10.1109/RIOS.2015.7270748(1-5)Online publication date: 12-Apr-2015
https://doi.org/10.1109/RIOS.2015.7270748
Juarez MTorra V(2014)DisPA: An Intelligent Agent for Private Web SearchAdvanced Research in Data Privacy10.1007/978-3-319-09885-2_21(389-405)Online publication date: 22-Aug-2014
https://doi.org/10.1007/978-3-319-09885-2_21
Show More Cited By

Index Terms

PQC: personalized query classification
1. Information systems
  1. Information retrieval

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Published In

cover image ACM Conferences

CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

November 2009

2162 pages

ISBN:9781605585123

DOI:10.1145/1645953

General Chairs:
David Cheung
University of Hong Kong, Hong Kong
,
Il-Yeol Song
Drexel University, USA
,
Program Chairs:
Wesley Chu
UCLA, USA
,
Xiaohua Hu
Drexel University, USA
,
Jimmy Lin
University of Maryland, USA

Copyright © 2009 ACM.

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]

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Publication History

Published: 02 November 2009

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November 2 - 6, 2009

Hong Kong, China

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Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Citations

Cited By

Zhang HSong WLiu LDu CZhao X(2017)Query Classification Using Convolutional Neural Networks2017 10th International Symposium on Computational Intelligence and Design (ISCID)10.1109/ISCID.2017.212(441-444)Online publication date: Dec-2017
https://doi.org/10.1109/ISCID.2017.212
Rashidghalam HMahmoudi F(2015)Web query classification using improved visiting probability algorithm and babelnet semantic graph2015 AI & Robotics (IRANOPEN)10.1109/RIOS.2015.7270748(1-5)Online publication date: 12-Apr-2015
https://doi.org/10.1109/RIOS.2015.7270748
Juarez MTorra V(2014)DisPA: An Intelligent Agent for Private Web SearchAdvanced Research in Data Privacy10.1007/978-3-319-09885-2_21(389-405)Online publication date: 22-Aug-2014
https://doi.org/10.1007/978-3-319-09885-2_21
Yu HRen F(2014)Subtopic Mining via Modifier Graph ClusteringAdvances in Knowledge Discovery and Data Mining10.1007/978-3-319-06608-0_28(337-347)Online publication date: 2014
https://doi.org/10.1007/978-3-319-06608-0_28
Yu HSakai T(2013)User-Aware AdvertisabilityInformation Retrieval Technology10.1007/978-3-642-45068-6_39(452-463)Online publication date: 2013
https://doi.org/10.1007/978-3-642-45068-6_39
Aktolga EJain AVelipasaoglu E(2013)Building Rich User Search Queries ProfilesUser Modeling, Adaptation, and Personalization10.1007/978-3-642-38844-6_21(254-266)Online publication date: 2013
https://doi.org/10.1007/978-3-642-38844-6_21
Juárez MTorra V(2013)Toward a Privacy Agent for Information RetrievalInternational Journal of Intelligent Systems10.1002/int.2159528:6(606-622)Online publication date: 26-Mar-2013
https://doi.org/10.1002/int.21595
Guo JCheng XXu GZhu X(2011)Intent-aware query similarityProceedings of the 20th ACM international conference on Information and knowledge management10.1145/2063576.2063619(259-268)Online publication date: 24-Oct-2011
https://dl.acm.org/doi/10.1145/2063576.2063619

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