poster

Dynamic query intent mining from a search log stream

Authors:

Cong LiAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1205 - 1208

https://doi.org/10.1145/2505515.2507856

Published: 27 October 2013 Publication History

Get Access

Abstract

It has long been recognized that search queries are often broad and ambiguous. Even when submitting the same query, different users may have different search intents. Moreover, the intents are dynamically evolving. Some intents are constantly popular with users, others are more bursty. We propose a method for mining dynamic query intents from search query logs. By regarding the query logs as a data stream, we identify constant intents while quickly capturing new bursty intents. To evaluate the accuracy and efficiency of our method, we conducted experiments using 50 topics from the NTCIR INTENT-9 data and additional five popular topics, all supplemented with six-month query logs from a commercial search engine. Our results show that our method can accurately capture new intents with short response time.

References

[1]

Anagha Kulkarni, Jaime Teevan, Krysta M Svore, and Susan T Dumais. Understanding temporal query dynamics. In Proceedings of the WSDM'11, pages 167--176, 2011.

Digital Library

Google Scholar

[2]

Charles L. A. Clarke, Nick Craswell, and Ian Soboroff. Overview of the TREC 2009 web track. In Proceedings of TREC'09, pages 1--9, 2009.

Google Scholar

[3]

Ruihua Song, Min Zhang, Tetsuya Sakai, Makoto P Kato, Yiqun Liu, Miho Sugimoto, Qinglei Wang, and Naoki Orii. Overview of the NTCIR-9 INTENT task. Proceedings of NTCIR-9, pages 82--105, 2011.

Google Scholar

[4]

Xuanhui Wang and Chengxiang Zhai. Learn from web search logs to organize search results. In Proceedings SIGIR'07, pages 87--94, 2007.

Digital Library

Google Scholar

[5]

Doug Beeferman and Adam L. Berger. Agglomerative clustering of a search engine query log. In Proceedings of SIGKDD'00, pages 407--416, 2000.

Digital Library

Google Scholar

[6]

Filip Radlinski, Martin Szummer, and Nick Craswell. Inferring query intent from reformulations and clicks. In Proceedings of WWW'10, pages 1171--1172, 2010.

Digital Library

Google Scholar

[7]

Qiaozhu Mei, Chao Liu, Hang Su, and ChengXiang Zhai. A probabilistic approach to spatiotemporal theme pattern mining on weblogs. In Proceedings of the WWW'06, pages 533--542, 2006.

Digital Library

Google Scholar

[8]

Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, and Hang Li. Context-aware query suggestion by mining click-through and session data. In Proceedings of SIGKDD'08, pages 875--883, 2008.

Digital Library

Google Scholar

[9]

Milad Shokouhi and Kira Radinsky. Time-sensitive query auto-completion. In Proceedings of SIGIR'12, pages 601--610, 2012.

Digital Library

Google Scholar

[10]

Yunhua Hu, Yanan Qian, Hang Li, Daxin Jiang, Jian Pei, and Qinghua Zheng. Mining query subtopics from search log data. In Proceedings of SIGIR'12, pages 305--314, 2012.

Digital Library

Google Scholar

[11]

Enrique Amigó, Julio Gonzalo, Javier Artiles, and Felisa Verdejo. A comparison of extrinsic clustering evaluation metrics based on formal constraints. Information retrieval, 12(4):461--486, 2009.

Digital Library

Google Scholar

Cited By

View all

Li KYu ZZhang YGuo B(2023)Impact of COVID-19 Pandemic on Cultural Products InterestsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587594(1190-1195)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587594
Rashid AFarooq MAbid AUmer TBashir AZikria Y(2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
https://doi.org/10.1007/s40747-021-00342-9
Zhang RGuo JFan YLan YCheng Xd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Query Understanding via Intent Description GenerationProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411999(1823-1832)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411999
Show More Cited By

Index Terms

Dynamic query intent mining from a search log stream
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

Recommendations

Dynamic Query Intent Prediction from a Search Log Stream

The users that used search engines are obligated to express their goals in few words queries. Sometimes search queries are ambiguous. Moreover, the users' intents are dynamically evolving. This paper analyzes the user's query logs to classify the ...
Mining query subtopics from search log data
SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Most queries in web search are ambiguous and multifaceted. Identifying the major senses and facets of queries from search log data, referred to as query subtopic mining in this paper, is a very important issue in web search. Through search log analysis, ...
Intent mining in search query logs for automatic search script generation

Capturing users' information needs is essential in decreasing the barriers in information access. This paper mines sequences of actions called search scripts from search query logs which keep large-scale users' search experiences. Search scripts can ...

Comments

Information & Contributors

Information

Published In

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

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: 27 October 2013

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM'13

Sponsor:

CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

Acceptance Rates

CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

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

Upcoming Conference

CIKM '25

Sponsor:
sigir
sigir

The 34th ACM International Conference on Information and Knowledge Management

November 10 - 14, 2025

Seoul , Republic of Korea

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

16
Total Citations
View Citations
392
Total Downloads

Downloads (Last 12 months)3
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Li KYu ZZhang YGuo B(2023)Impact of COVID-19 Pandemic on Cultural Products InterestsCompanion Proceedings of the ACM Web Conference 202310.1145/3543873.3587594(1190-1195)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543873.3587594
Rashid AFarooq MAbid AUmer TBashir AZikria Y(2021)Social media intention mining for sustainable information systems: categories, taxonomy, datasets and challengesComplex & Intelligent Systems10.1007/s40747-021-00342-99:3(2773-2799)Online publication date: 5-Apr-2021
https://doi.org/10.1007/s40747-021-00342-9
Zhang RGuo JFan YLan YCheng Xd'Aquin MDietze SHauff CCurry ECudre Mauroux P(2020)Query Understanding via Intent Description GenerationProceedings of the 29th ACM International Conference on Information & Knowledge Management10.1145/3340531.3411999(1823-1832)Online publication date: 19-Oct-2020
https://dl.acm.org/doi/10.1145/3340531.3411999
Shajalal MAono M(2020)Coverage-based query subtopic diversification leveraging semantic relevanceKnowledge and Information Systems10.1007/s10115-020-01470-362:7(2873-2891)Online publication date: 27-Apr-2020
https://doi.org/10.1007/s10115-020-01470-3
Gaou SFihri M(2020)A New Approach of a List of Items for Search Retrieval SystemsAdvanced Intelligent Systems for Sustainable Development (AI2SD’2019)10.1007/978-3-030-36674-2_6(53-66)Online publication date: 6-Feb-2020
https://doi.org/10.1007/978-3-030-36674-2_6
Kang XWu YRen F(2020)Toward action comprehension for searchingJournal of the Association for Information Science and Technology10.1002/asi.2422071:2(143-157)Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1002/asi.24220
Zouhair AGaou SEl Mokhtar ECastillo-Valdivieso PBertelle CLazaar MDuvallet CAl Achhab MMahboub OSilkan H(2019)A New Approach for Search Engine Optimization based Case Based Reasoning to detect user's intentProceedings of the 4th International Conference on Big Data and Internet of Things10.1145/3372938.3372975(1-8)Online publication date: 23-Oct-2019
https://dl.acm.org/doi/10.1145/3372938.3372975
Reddy K(2019)Query log streams based incremental schema matchingProceedings of the Australasian Computer Science Week Multiconference10.1145/3290688.3290713(1-4)Online publication date: 29-Jan-2019
https://dl.acm.org/doi/10.1145/3290688.3290713
Kumar VSingh V(2019)An Automatic Intent Modeling Algorithm for Interactive Data ExplorationComputational Intelligence in Data Mining10.1007/978-981-13-8676-3_12(127-140)Online publication date: 18-Aug-2019
https://doi.org/10.1007/978-981-13-8676-3_12
(2018)Hierarchical classification of web search results to detect usersInternational Journal of Artificial Intelligence and Soft Computing10.5555/3309394.33093966:4(287-305)Online publication date: 1-Jan-2018
https://dl.acm.org/doi/10.5555/3309394.3309396
Show More Cited By

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

Dynamic Query Intent Prediction from a Search Log Stream

Mining query subtopics from search log data

Intent mining in search query logs for automatic search script generation