poster

Ranking model selection and fusion for effective microblog search

Authors:

Tarek El-Ganainy,

Kam-Fai WongAuthors Info & Claims

SoMeRA '14: Proceedings of the first international workshop on Social media retrieval and analysis

Pages 21 - 26

https://doi.org/10.1145/2632188.2632202

Published: 11 July 2014 Publication History

Abstract

Re-ranking was shown to have positive impact on the effectiveness for microblog search. Yet existing approaches mostly focused on using a single ranker to learn some better ranking function with respect to various relevance features. Given various available rank learners (such as learning to rank algorithms), in this work, we mainly study an orthogonal problem where multiple learned ranking models form an ensemble for re-ranking the retrieved tweets than just using a single ranking model in order to achieve higher search effectiveness. We explore the use of query-sensitive model selection and rank fusion methods based on the result lists produced from multiple rank learners. Base on the TREC microblog datasets, we found that our selection-based ensemble approach can significantly outperform using the single best ranker, and it also has clear advantage over the rank fusion that combines the results of all the available models.

References

[1]

J.A. Aslam and M. Montague. Models for Metasearch. In Proceedings of SIGIR, pp.276--284, 2001.

Digital Library

[2]

L. Breiman. Random forests. Machine learning, 45(1):5--32, 2001.

Digital Library

[3]

C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender. Learning to rank using gradient descent. In Proceedings of ICML, pp.89--96, 2005.

Digital Library

[4]

G. Cormack, C. Clarke, and S. Buettcher. Reciprocal rank fusion outperforms condorcet and individual rank learning methods. In Proceedings of SIGIR, pp.758--759, 2009.

Digital Library

[5]

G. Crestani. Combination of similarity measures for effective spoken document retrieval. Journal of Information Science, 29(2):87--96, 2003.

[6]

S. Deerwester, S. Dumais, G. Furnas, T. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391--407, 1990.

[7]

M. Efron, P. Organisciak, and K. Fenlon. Improving retrieval of short text through document expansion. In Proceedings of SIGIR, pp.911--920, 2012.

Digital Library

[8]

A. Din and W. Magdy. Web-based pseudo relevance feedback for microblog retrieval. In Proceedings of TREC, 2012.

[9]

Y. Duan, L. Jiang, T. Qin, M. Zhou, and H.Y. Shum. An empirical study on learning to rank of tweets. In Proceedings of COLING, pp.295--303, 2010.

Digital Library

[10]

T. El-Ganainy, Z. Wei, W. Magdy, and W. Gao. QCRI at TREC 2013 Microblog Track. In Proceedings of TREC, 2013.

[11]

E.A. Fox and J.A. Shaw. Combination of Multiple Searches. In Proceedings of TREC, pp.243--252, 1994.

[12]

Y. Freund, R. Iyer, R.E. Schapire, Y. Singer. An efficient boosting algorithm for combining preferences. Journal of Machine Learning Research, 4:933--969, 2003.

Digital Library

[13]

J.H. Friedman. Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29(5):1189--1232, 2001.

[14]

W. Gao, Z. Wei, and K.F. Wong. Microblog Search and Filtering with Time Sensitive Feedback and Thresholding based on BM25. In Proceedings of TREC, 2012.

[15]

Z. Han, X. Li, M. Yang, H. Qi, S. Li, and T. Zhao. HIT at TREC 2012 microblog track. In Proceedings of TREC, 2012.

[16]

S. Kullback. Information theory and statistics. Dover Publications Inc. (1997).

[17]

J.H. Lee. Analyses of multiple evidence combination. In Proceedings of SIGIR, pp.267--275, 1997.

Digital Library

[18]

J. Lin and M. Efron. Overview of the TREC2013 Microblog Track. In Proceedings of TREC, 2013.

[19]

T.Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3(3):225--331, 2009.

Digital Library

[20]

A. Marcus, M. Bernstein, O. Badar, D. Karger, S. Madden, and R. Miller. Twitinfo: aggregating and visualizing microblogs for event exploration. In Proceedings of SIGCHI, pp.227--236, 2011.

Digital Library

[21]

D. Metzler and W.B. Croft. Linear feature-based models for information retrieval. Information Retrieval, 10(3):257--274, 2007.

Digital Library

[22]

D. Metzler and C. Cai. USC/ISI at TREC 2011: Microblog track. In Proceedings of TREC, 2011.

[23]

M. Montague and J.A. Aslam. Condorcet fusion for improved retrieval. In Proceedings of CIKM, pp.538--548, 2002.

Digital Library

[24]

N. Naveed, T. Gottron, J. Kunegis, A.C. Alhadi. Searching microblogs: coping with sparsity and document quality. In Proceedings of CIKM, pp.183--188, 2011.

Digital Library

[25]

I. Ounis, C. Macdonald, J. Lin, and I. Soboroff. Overview of the TREC-2011 microblog track. In Proceedings of TREC, 2011.

[26]

J. Peng, C. Macdonald, and I. Ounis. Learning to select a ranking function. In Proceedings of ECIR, pp.114--126, 2010.

Digital Library

[27]

J. Ponte and W.B. Croft. A language modeling approach to information retrieval. In Proceedings of SIGIR, pp.275--281, 1998.

Digital Library

[28]

I. Soboroff, I. Ounis, C. Macdonald, and J. Lin. Overview of the TREC-2012 microblog track. In Proceedings of TREC, 2012.

[29]

J. Teevan, D. Ramage, and M.R. Morris.# twittersearch: a comparison of microblog search and web search. In Proceedings of WSDM, pp.35--44, 2011.

Digital Library

[30]

Z. Wei, W. Gao, L. Zhou, B. Li, and K.F. Wong. Exploring tweets normalization and query time sensitivity for twitter search. In Proceedings of TREC 2011.

[31]

Q. Wu, C.J. Burges, K.M. Svore, and J. Gao. Adapting boosting for information retrieval measures. Information Retrieval, 13(3):254--270, 2010.

Digital Library

[32]

J. Xu. Solving the word mismatch problem through automatic text analysis. PhD Dissertation, University of Massachusetts, Amherst, 1997.

Digital Library

[33]

C. Zhai and J. Lafferty. Model-based feedback in the language modeling approach to information retrieval. In Proceedings of CIKM, pp.403--410, 2001.

Digital Library

Cited By

Hlaoua L(2024)An overview of aggregation methods for social networks analysisKnowledge and Information Systems10.1007/s10115-024-02296-z67:1(1-28)Online publication date: 12-Dec-2024
https://doi.org/10.1007/s10115-024-02296-z
Zhang ZWu S(2023)Data Fusion Performance Prophecy: A Random Forest RevelationInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_20(192-200)Online publication date: 22-Nov-2023
https://doi.org/10.1007/978-3-031-48316-5_20
Rasheed IBanka HKhan H(2021)Pseudo-relevance feedback based query expansion using boosting algorithmArtificial Intelligence Review10.1007/s10462-021-09972-4Online publication date: 20-Feb-2021
https://doi.org/10.1007/s10462-021-09972-4
Show More Cited By

Index Terms

Ranking model selection and fusion for effective microblog search
1. Information systems
  1. Information retrieval

Recommendations

Improving Ranking Consistency for Web Search by Leveraging a Knowledge Base and Search Logs
CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

In this paper, we propose a new idea called ranking consistency in web search. Relevance ranking is one of the biggest problems in creating an effective web search system. Given some queries with similar search intents, conventional approaches typically ...
Effective rank aggregation for metasearching

Nowadays, mashup services and especially metasearch engines play an increasingly important role on the Web. Most of users use them directly or indirectly to access and aggregate information from more than one data sources. Similarly to the rest of the ...
Time-Aware Rank Aggregation for Microblog Search
CIKM '14: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

We tackle the problem of searching microblog posts and frame it as a rank aggregation problem where we merge result lists generated by separate rankers so as to produce a final ranking to be returned to the user. We propose a rank aggregation method, ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

SoMeRA '14: Proceedings of the first international workshop on Social media retrieval and analysis

July 2014

72 pages

ISBN:9781450330220

DOI:10.1145/2632188

Program Chairs:
Markus Schedl
Johannes Kepler University Linz, Austria
,
Peter Knees
Johannes Kepler University Linz, Austria
,
Jialie Shen
Singapore Management University, Singapore

Copyright © 2014 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]

Sponsors

SIGIR: ACM Special Interest Group on Information Retrieval

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 11 July 2014

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

SIGIR '14

Sponsor:

SIGIR

SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11, 2014

Queensland, Gold Coast, Australia

Acceptance Rates

SoMeRA '14 Paper Acceptance Rate 13 of 19 submissions, 68%;

Overall Acceptance Rate 13 of 19 submissions, 68%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

8
Total Citations
View Citations
142
Total Downloads

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

Reflects downloads up to 08 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

Hlaoua L(2024)An overview of aggregation methods for social networks analysisKnowledge and Information Systems10.1007/s10115-024-02296-z67:1(1-28)Online publication date: 12-Dec-2024
https://doi.org/10.1007/s10115-024-02296-z
Zhang ZWu S(2023)Data Fusion Performance Prophecy: A Random Forest RevelationInformation Integration and Web Intelligence10.1007/978-3-031-48316-5_20(192-200)Online publication date: 22-Nov-2023
https://doi.org/10.1007/978-3-031-48316-5_20
Rasheed IBanka HKhan H(2021)Pseudo-relevance feedback based query expansion using boosting algorithmArtificial Intelligence Review10.1007/s10462-021-09972-4Online publication date: 20-Feb-2021
https://doi.org/10.1007/s10462-021-09972-4
Alshalan SAlshalan RAl-Khalifa HSuwaileh RElsayed T(2020)Improving Arabic Microblog Retrieval with Distributed RepresentationsInformation Retrieval Technology10.1007/978-3-030-42835-8_16(185-194)Online publication date: 27-Feb-2020
https://doi.org/10.1007/978-3-030-42835-8_16
Singh JSharan A(2018)Rank fusion and semantic genetic notion based automatic query expansion modelSwarm and Evolutionary Computation10.1016/j.swevo.2017.09.00738(295-308)Online publication date: Feb-2018
https://doi.org/10.1016/j.swevo.2017.09.007
Alshamrani AChowdhary APisharody SLu DHuang DCuevas ÁDe Grande RDarehshoorzadeh A(2017)A Defense System for Defeating DDoS Attacks in SDN based NetworksProceedings of the 15th ACM International Symposium on Mobility Management and Wireless Access10.1145/3132062.3132074(83-92)Online publication date: 21-Nov-2017
https://dl.acm.org/doi/10.1145/3132062.3132074
Chen JYu H(2017)Unsupervised ensemble ranking of terms in electronic health record notes based on their importance to patientsJournal of Biomedical Informatics10.1016/j.jbi.2017.02.01668:C(121-131)Online publication date: 1-Apr-2017
https://dl.acm.org/doi/10.1016/j.jbi.2017.02.016
Singh JPrasad MPrasad OMeng Joo ESaxena ALin C(2016)A Novel Fuzzy Logic Model for Pseudo-Relevance Feedback-Based Query ExpansionInternational Journal of Fuzzy Systems10.1007/s40815-016-0254-118:6(980-989)Online publication date: 4-Oct-2016
https://doi.org/10.1007/s40815-016-0254-1

View Options

Login options

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

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten