research-article

Recommending questions using the mdl-based tree cut model

Authors:

Hsiao-Wuen HonAuthors Info & Claims

WWW '08: Proceedings of the 17th international conference on World Wide Web

Pages 81 - 90

https://doi.org/10.1145/1367497.1367509

Published: 21 April 2008 Publication History

Abstract

The paper is concerned with the problem of question recommendation. Specifically, given a question as query, we are to retrieve and rank other questions according to their likelihood of being good recommendations of the queried question. A good recommendation provides alternative aspects around users' interest. We tackle the problem of question recommendation in two steps: first represent questions as graphs of topic terms, and then rank recommendations on the basis of the graphs. We formalize both steps as the tree-cutting problems and then employ the MDL (Minimum Description Length) for selecting the best cuts. Experiments have been conducted with the real questions posted at Yahoo! Answers. The questions are about two domains, 'travel' and 'computers & internet'. Experimental results indicate that the use of the MDL-based tree cut model can significantly outperform the baseline methods of word-based VSM or phrase-based VSM. The results also show that the use of the MDL-based tree cut model is essential to our approach.

References

[1]

Banerjee, S. and Pedersen, T. The design, implementation, and use of the ngram statistics package. In Proc. of the 4th CICLing'03.

Digital Library

[2]

Barron, A., Rissanen, J., and Yu, B. The minimum description length principle in coding and modeling. IEEE Trans. Information Theory, vol. 44 (1998), pp. 2743--2760.

Digital Library

[3]

Burke, R. D., Hammond, K. J., Kulyukin, V. A., Lytinen, S. L., Tomuro, N., and Schoenberg, S. Question answering from frequently asked question files: Experiences with the faq finder system. Technical report, 1997.

Digital Library

[4]

Cao, Y. and Li, H. Base noun phrase translation using web data and the EM algorithm. In Proc. of COLING'02.

Digital Library

[5]

Christopher, M. D. and Hinrich, S. Foundations of Statistical Natural Language Processing. MIT Press: 1999.

Digital Library

[6]

Church, K. W. and Hanks, P. Word association norms, mutual information, and lexicography. In Proc. of ACL'89.

Digital Library

[7]

Cuerzan, S. and White, R. W. Query Suggestion based on landing pages. In Proc. of SIGIR'07.

Digital Library

[8]

Fellbaum, C. WordNet: An Electronic Lexical Database. MIT Press, 1998.

[9]

Fonseca, B. M., Golgher, P. B., Moura, E. S., Possas, B., and Ziviani, N. Discovering search engine related queries using association rules. Journal of Web Engineering, 2003.

Digital Library

[10]

Fredkin, E. Trie Memory. Communications of the ACM, D. 3(9):490--499, 1960.

Digital Library

[11]

Gleich, D. and Zhukov, L. SVD based term suggestion and ranking system. In Proc. of ICDM'04.

Digital Library

[12]

Jensen, E. C., Beitzel, S. M., Chowdhury, A., and Frieder, O. Query phrase suggestion from topically tagged session logs. In Proc. of FQAS'06.

Digital Library

[13]

Jeon, J. and Croft, W. B. Learning translation-based language models using Q&A archives. Technical Report, University of Massachusetts.

[14]

Jeon, J., Croft, W. B., and Lee, J. Finding similar questions in large question and answer archives. In Proc. of CIKM'05.

Digital Library

[15]

Jeon, J., Croft, W. B., and Lee, J. H. Finding semantically similar questions based on their answers. In Proc. of SIGIR'05.

Digital Library

[16]

Jones, R., Rey, B., Madani, O., and Greiner, W. Generating query substitutions. In Proc. of WWW'06.

Digital Library

[17]

Kawamae, N., Suzuki, H., and Mizuno, O. Query and content suggestion based on latent interest and topic class. In Proc. of WWW'04.

[18]

Lai, Y.-S., Fung, K.-A., and Wu, C.-H. Faq mining via list detection. In Proc. of the Workshop on Multilingual Summarization and Question Answering, 2002.

Digital Library

[19]

Li, H. and Abe, N. Generalizing Case Frames using a thesaurus and the MDL principle. Computational Linguistics, 24(2), pp.217--244, 1998.

Digital Library

[20]

Rissanen, J. Modeling by shortest data description. Automatica, vol. 14 (1978), pp. 465--471.

Digital Library

[21]

Rissanen, J. Universal coding information, prediction and estimation. IEEE Transaction on Information Theory, vol. 30(4): 629--636.

Digital Library

[22]

Salton, G., Wong, A., and Yang, C. S. A vector space model for automatic indexing. Communications of the ACM, vol. 18, nr. 11, pages 613--620.

Digital Library

[23]

Sneiders, E. Automated question answering using question templates that cover the conceptual model of the database. In Proc. of the 6th NLDB'02.

Digital Library

[24]

Wen, J. R., Nie, J.-Y., and Zhang, H. J. Query clustering using user logs. ACM Trans. Information Systems, 20(1):59--81, 2002.

Digital Library

[25]

Xun, E. D., Huang, C.N., and Zhou, M. A unified statistical model for the identification of English BaseNP. In Proc. of ACL'00.

Digital Library

Cited By

Castro JYera Toledo RAlzahrani ASanchez PBarranco MMartinez L(2019)A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer ItemsIEEE Access10.1109/ACCESS.2019.29578817(182664-182678)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957881
Tymoshenko KMoschitti A(2018)Shallow and Deep Syntactic/Semantic Structures for Passage Reranking in Question-Answering SystemsACM Transactions on Information Systems10.1145/323377237:1(1-38)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3233772
Nguyen VLe AVu D(2018)An Efficient Model for Finding and Ranking Related Questions in Community Question Answering SystemsInformation Systems Design and Intelligent Applications10.1007/978-981-10-7512-4_76(776-786)Online publication date: 2-Mar-2018
https://doi.org/10.1007/978-981-10-7512-4_76
Show More Cited By

Index Terms

Recommending questions using the mdl-based tree cut model
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information retrieval
    1. Information retrieval query processing
  2. Information systems applications

Recommendations

Learning to recommend questions based on public interest
CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

This paper is concerned with the problem of question recommendation in the setting of Community Question Answering (CQA). Given a question as query, our goal is to rank all of the retrieved questions according to their likelihood of being good ...
Learning to recommend questions based on user ratings
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management

At community question answering services, users are usually encouraged to rate questions by votes. The questions with the most votes are then recommended and ranked on the top when users browse questions by category. As users are not obligated to rate ...
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

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '08: Proceedings of the 17th international conference on World Wide Web

April 2008

1326 pages

ISBN:9781605580852

DOI:10.1145/1367497

General Chairs:
Jinpeng Huai
Beihang University, China
,
Robin Chen
AT&T Labs, USA
,
Hsiao-Wuen Hon
Microsoft Research Asia, China
,
Yunhao Liu
HK University of Science and Technology, Hong Kong
,
Program Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Andrew Tomkins
Yahoo! Research, USA
,
Xiaodong Zhang
The Ohio State University, USA

Copyright © 2008 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

ACM: Association for Computing Machinery

In-Cooperation

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 21 April 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

WWW '08

Sponsor:

ACM

WWW '08: The 17th International World Wide Web Conference

April 21 - 25, 2008

Beijing, China

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

33
Total Citations
View Citations
528
Total Downloads

Downloads (Last 12 months)4
Downloads (Last 6 weeks)3

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Castro JYera Toledo RAlzahrani ASanchez PBarranco MMartinez L(2019)A Big Data Semantic Driven Context Aware Recommendation Method for Question-Answer ItemsIEEE Access10.1109/ACCESS.2019.29578817(182664-182678)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2957881
Tymoshenko KMoschitti A(2018)Shallow and Deep Syntactic/Semantic Structures for Passage Reranking in Question-Answering SystemsACM Transactions on Information Systems10.1145/323377237:1(1-38)Online publication date: 19-Nov-2018
https://dl.acm.org/doi/10.1145/3233772
Nguyen VLe AVu D(2018)An Efficient Model for Finding and Ranking Related Questions in Community Question Answering SystemsInformation Systems Design and Intelligent Applications10.1007/978-981-10-7512-4_76(776-786)Online publication date: 2-Mar-2018
https://doi.org/10.1007/978-981-10-7512-4_76
Nguyen VLe A(2018)Deep Neural Network-Based Models for Ranking Question - Answering Pairs in Community Question Answering SystemsIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-319-75429-1_15(179-190)Online publication date: 4-Feb-2018
https://doi.org/10.1007/978-3-319-75429-1_15
Da San Martino GRomeo SBarroón-Cedeño AJoty SMaàrquez LMoschitti ANakov PKando NSakai TJoho HLi Hde Vries AWhite R(2017)Cross-Language Question Re-RankingProceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3077136.3080743(1145-1148)Online publication date: 7-Aug-2017
https://dl.acm.org/doi/10.1145/3077136.3080743
Srba IBielikova M(2016)A Comprehensive Survey and Classification of Approaches for Community Question AnsweringACM Transactions on the Web10.1145/293468710:3(1-63)Online publication date: 16-Aug-2016
https://dl.acm.org/doi/10.1145/2934687
Pal A(2015)Metrics and Algorithms for Routing Questions to User CommunitiesACM Transactions on Information Systems10.1145/272470633:3(1-29)Online publication date: 9-Mar-2015
https://dl.acm.org/doi/10.1145/2724706
Burel GMulholland PHe YAlani HYesilada YFarzan RHouben G(2015)Predicting Answering Behaviour in Online Question Answering CommunitiesProceedings of the 26th ACM Conference on Hypertext & Social Media10.1145/2700171.2791041(201-210)Online publication date: 24-Aug-2015
https://dl.acm.org/doi/10.1145/2700171.2791041
Cho JLi YGirju RZhai C(2015)Recommending forum posts to designated expertsProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7363810(659-666)Online publication date: 29-Oct-2015
https://dl.acm.org/doi/10.1109/BigData.2015.7363810
Zhou TLyu MKing ILou J(2015)Learning to suggest questions in social mediaKnowledge and Information Systems10.1007/s10115-014-0737-z43:2(389-416)Online publication date: 1-May-2015
https://dl.acm.org/doi/10.1007/s10115-014-0737-z
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