More Web Proxy on the site http://driver.im/

research-article

Dual role model for question recommendation in community question answering

Authors:

Bin WangAuthors Info & Claims

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

Pages 771 - 780

https://doi.org/10.1145/2348283.2348387

Published: 12 August 2012 Publication History

Abstract

Question recommendation that automatically recommends a new question to suitable users to answer is an appealing and challenging problem in the research area of Community Question Answering (CQA). Unlike in general recommender systems where a user has only a single role, each user in CQA can play two different roles (dual roles) simultaneously: as an asker and as an answerer. To the best of our knowledge, this paper is the first to systematically investigate the distinctions between the two roles and their different influences on the performance of question recommendation in CQA. Moreover, we propose a Dual Role Model (DRM) to model the dual roles of users effectively. With different indepen-dence assumptions, two variants of DRM are achieved. Finally, we present the DRM based approach to question recommendation which provides a mechanism for naturally integrating the user relation between the answerer and the asker with the content re-levance between the answerer and the question into a uni-fied probabilistic framework. Experiments using a real-world data crawled from Yahoo! Answers show that: (1) there are evident distinctions between the two roles of users in CQA. Additionally, the answerer role is more effective than the asker role for modeling candidate users in question recommendation; (2) compared with baselines utilizing a single role or blended roles based methods, our DRM based approach consistently and significantly improves the performance of question recommendation, demonstrating that our approach can model the user in CQA more reasonably and precisely.

References

[1]

L. Rao. Yahoo mail and im users update their status 800 million times a month. TechCrunch, Oct282009. http://techcrunch.com/2009/10/28/yahoo-mail-and-im-usersupdate-their-status-800-million-times-a-month/.

[2]

Hu Wu, Yongji Wang and Xiang Cheng. Incremental Probabilistic Latent Semantic Analysis for automatic question recommendation. In RecSys'08, pages 99--106, 2008.

Digital Library

[3]

Damon Horowitz and Sepandar D. Kamvar. The anatomy of a large-scale social search engine. In WWW'10, pages 431--440, 2010.

Digital Library

[4]

Jinwen Guo, Shengliang Xu, Shenghua Bao, and Yong Yu. Tapping on the potential of Q&A community by recommending answer providers. In CIKM'08, pages 921--930, 2008.

Digital Library

[5]

Gideon Dror, Yehuda Koren, Yoelle Maarek and Idan Szpektor. I want to answer, who has a question? Yahoo! Answers recommender system. In SIGKDD'11, pages 1109--1117, 2011.

Digital Library

[6]

Mingcheng Qu, Guang Qiu, Xiaofei He, Cheng Zhang, Hao Wu, Jiajun Bu, and Chun Chen. Probabilistic question recommendation for question answering communities. In WWW'09, pages 1229--1230, 2009.

Digital Library

[7]

Alexandrin Popescul, Lyle H. Ungar, David M. Pennock and Steve Lawrence. Probabilistic models for unified collaborative and content-based recommendation in sparse-data environments. In UAI'01, pages 437--444, 2001.

Digital Library

[8]

Baichuan Li, Irwin King and Michael R. Lyu. Question routing in community question answering- putting category in its place. In CIKM'11, pages 2041--2044, 2011.

Digital Library

[9]

Thomas Hofmann and Jan Puzicha. Latent class models for collaborative filtering. In IJCAI'99, pages 688--693. 1999.

Digital Library

[10]

Thomas Hofmann. Probabilistic latent semantic indexing. In SIGIR'99, pages 50--57, 1999.

Digital Library

[11]

Luo Si and Rong Jin. Flexible mixture model for collaborative filtering. In ICML'03, 2003.

[12]

David M. Blei, Andrew Y. Ng and Michael I. Jordan. La-tent dirichlet allocation. In Journal of Machine Learning Research, pages 993--1022, 2003.

Digital Library

[13]

Thomas Hofmann. Collaborative filtering via gaussian probabilistic latent semantic analysis. In SIGIR'03, pages 259--266, 2003.

Digital Library

[14]

Tom Chao Zhou, Chin-Yew Lin, IrwinKing, Michael R. Lyu, Young-In Song and Yunbo Cao. Learning to suggest questions in online forums. In AAAI'11, pages 1298--1303, 2011.

[15]

Qiaoling Liu and Eugene Agichtein. Modeling answerer behavior in collaborative question answering systems. In ECIR'11, pages 67--79, 2011.

Digital Library

[16]

Ke Sun, Yunbo Cao, Xinying Song, Young-In Song, Xiaolong Wang and Chin-Yew Lin. Learning to recommend questions based on user rating. In CIKM'09, pages 751--758, 2009.

Digital Library

[17]

Nathan N. Liu and Qiang Yang. EigenRank: A ranking-oriented approach to collaborative filtering. In SIGIR'08, pages 83--90, 2008.

Digital Library

[18]

Adomavicius G., and Tuzhilin A. Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions. In IEEE Trans. on Knowledge and Data Engineering, 17(6), 2005.

Digital Library

[19]

P. Kantor, F. Ricci, L. Rokach, and B. Shapira. Recommender Systems Handbook: A complete guide for research scientists and practitioners. Springer, 2010.

Digital Library

[20]

Thomas Hofmann. Unsupervised learning by probabilistic latent semantic analysis. Maching Learning Journal, Vol. 42, No. 1--2, pages. 177--196, 2001.

Digital Library

[21]

Jiwoon Jeon, W. Bruce Croft, Joon Ho Lee and Soyeon Park. A framework to predict the quality of answers with non-textual features. In SIGIR'06, pages 228--235, 2006.

Digital Library

[22]

Jiwoon Jeon, W. Bruce Croft and Joon Ho Lee. Finding semantically similar questions based on their answers. In SIGIR'05, pages 617--618, 2005.

Digital Library

[23]

G. Linden, B. Smith, and J. York. Amazon.com recommendations: Item-to-item collaborative filtering. In IEEE Internet Computing, 07(1):76--80, 2003.

Digital Library

[24]

Jiahui Liu, Peter Dolan, Elin Rønby Pedersen. Personalized news recommendation based on click behavior. In IUI'10, pages 31--40, 2010.

Digital Library

[25]

Y. Cao, H. Duan, Chin-Yew Lin, Y. Yu and Hsiao-Wuen Hon. Recommending questions using the MDL-based tree cut model. In WWW'08, pages 81--90, 2008.

Digital Library

[26]

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

[27]

J. Zhang, L. A. Adamic, E. Bakshy and Mark S. Ackerman. Everyone knows something: Examining knowledge sharing on Yahoo! Answers. In WWW'08, pages 665--674, 2008.

Digital Library

[28]

M. Pazzani and D. Billsus. Learning and revising user profiles: The identification of interesting web sites. Machine Learning. vol. 27, pages 313--331, 1997.

Digital Library

[29]

Y. Koren, R. M. Bell, and C. Volinsky. Matrix factorization techniques for recommender systems. Journal of Computer, 42(8):30--37, 2009.

Digital Library

[30]

Kevin K. Nam, Mark S. Ackerman, Lada A. Adamic. Questions in, knowledge in? A study of Naver's question answer-ing community. In CHI'09, pages 779--788, 2009.

Digital Library

[31]

Pawel Jurczyk, Eugene Agichtein. Discovering authorities in question answer communities by using link analysis. In CIKM'07, pages 919--922, 2007.

Digital Library

Cited By

Andreasen TBordogna GTré GKacprzyk JLarsen HZadrożny S(2024)The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysisData & Knowledge Engineering10.1016/j.datak.2023.102246149(102246)Online publication date: Jan-2024
https://doi.org/10.1016/j.datak.2023.102246
Tang BGao QCui XPeng QYu X(2023)Feature-Alignment-Based Cross-Platform Question Answering Expert RecommendationMathematics10.3390/math1109217411:9(2174)Online publication date: 5-May-2023
https://doi.org/10.3390/math11092174
Kasela PPasi GPerego R(2023)SE-PEF: a Resource for Personalized Expert FindingProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625335(288-309)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625335
Show More Cited By

Index Terms

Dual role model for question recommendation in community question answering
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

Probabilistic question recommendation for question answering communities
WWW '09: Proceedings of the 18th international conference on World wide web

User-Interactive Question Answering (QA) communities such as Yahoo! Answers are growing in popularity. However, as these QA sites always have thousands of new questions posted daily, it is difficult for users to find the questions that are of interest ...
Question recommendation for collaborative question answering systems with RankSLDA
RecSys '14: Proceedings of the 8th ACM Conference on Recommender systems

Collaborative question answering (CQA) communities rely on user participation for their success. This paper presents a supervised Bayesian approach to model expertise in on-line CQA communities with application to question recommendation, aimed at ...
User correlation model for question recommendation in community question answering
Abstract
In this paper, we address the problem of question recommendation that automatically recommends a new question to suitable users to answer in community question answering (CQA). The major challenge of question recommendation is the accurate ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

SIGIR '12: Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval

August 2012

1236 pages

ISBN:9781450314725

DOI:10.1145/2348283

General Chair:
William Hersh
Oregon Health & Science University, USA
,
Program Chairs:
Jamie Callan
Carnegie Mellon University, USA
,
Yoelle Maarek
Yahoo! Research, Israel
,
Mark Sanderson
Royal Melbourne Institute of Technology, Australia

Copyright © 2012 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: 12 August 2012

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

SIGIR '12

Sponsor:

SIGIR

SIGIR '12: The 35th International ACM SIGIR conference on research and development in Information Retrieval

August 12 - 16, 2012

Oregon, Portland, USA

Acceptance Rates

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

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

56
Total Citations
View Citations
803
Total Downloads

Downloads (Last 12 months)15
Downloads (Last 6 weeks)0

Reflects downloads up to 26 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Andreasen TBordogna GTré GKacprzyk JLarsen HZadrożny S(2024)The power and potentials of Flexible Query Answering Systems: A critical and comprehensive analysisData & Knowledge Engineering10.1016/j.datak.2023.102246149(102246)Online publication date: Jan-2024
https://doi.org/10.1016/j.datak.2023.102246
Tang BGao QCui XPeng QYu X(2023)Feature-Alignment-Based Cross-Platform Question Answering Expert RecommendationMathematics10.3390/math1109217411:9(2174)Online publication date: 5-May-2023
https://doi.org/10.3390/math11092174
Kasela PPasi GPerego R(2023)SE-PEF: a Resource for Personalized Expert FindingProceedings of the Annual International ACM SIGIR Conference on Research and Development in Information Retrieval in the Asia Pacific Region10.1145/3624918.3625335(288-309)Online publication date: 26-Nov-2023
https://dl.acm.org/doi/10.1145/3624918.3625335
Costa GOrtale R(2023) Ask and Ye shall be AnsweredInformation Fusion10.1016/j.inffus.2023.10185699:COnline publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1016/j.inffus.2023.101856
Costa GOrtale R(2023)Here are the answers. What is your question? Bayesian collaborative tag-based recommendation of time-sensitive expertise in question-answering communitiesExpert Systems with Applications10.1016/j.eswa.2023.120042225(120042)Online publication date: Sep-2023
https://doi.org/10.1016/j.eswa.2023.120042
Wang GCheng DXia DJiang H(2023)Swarm Intelligence Research: From Bio-inspired Single-population Swarm Intelligence to Human-machine Hybrid Swarm IntelligenceMachine Intelligence Research10.1007/s11633-022-1367-720:1(121-144)Online publication date: 10-Jan-2023
https://doi.org/10.1007/s11633-022-1367-7
Li NGuo BLiu YDing YXu EYao LYu Z(2022)Transfer how much: a fine-grained measure of the knowledge transferability of user behavior sequences in social networkData Mining and Knowledge Discovery10.1007/s10618-022-00857-w36:6(2214-2236)Online publication date: 17-Sep-2022
https://doi.org/10.1007/s10618-022-00857-w
Guessoum MDjiroun RBoukhalfa KBenkhelifa E(2022)Natural language why-question in Business Intelligence applications: model and recommendation approachCluster Computing10.1007/s10586-022-03593-425:6(3875-3898)Online publication date: 18-May-2022
https://doi.org/10.1007/s10586-022-03593-4
Askari AVerberne SPasi G(2022)Expert Finding in Legal Community Question AnsweringAdvances in Information Retrieval10.1007/978-3-030-99739-7_3(22-30)Online publication date: 5-Apr-2022
https://doi.org/10.1007/978-3-030-99739-7_3
Lin ZKe HWong NBai JSong YZhao HYe JDemartini GZuccon GCulpepper JHuang ZTong H(2021)Multi-Relational Graph based Heterogeneous Multi-Task Learning in Community Question AnsweringProceedings of the 30th ACM International Conference on Information & Knowledge Management10.1145/3459637.3482279(1038-1047)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3459637.3482279
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.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents