poster

Learning to recommend questions based on public interest

Authors:

Jun Wang,

Xia Hu,

Zhoujun Li,

Wenhan Chao,

Biyun HuAuthors Info & Claims

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

Pages 2029 - 2032

https://doi.org/10.1145/2063576.2063882

Published: 24 October 2011 Publication History

Get Access

Abstract

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 recommendations for the query. In this paper, we propose a notion of public interest, and show how public interest can boost the performance of question recommendation. In particular, to model public interest in question recommendation, we build a language model to combine relevance score to the query and popularity score regarding question popularity. Experimental results on Yahoo!Answers dataset demonstrate the performance of question recommendation can be greatly improved with considering the public interest.

References

[1]

E. Agichtein, C. Castillo, and D. Donato. Finding high-quality content in social media. In WSDM'09, pages 183--193, 2009.

Digital Library

Google Scholar

[2]

S. Brin and L. Page. The anatomy of a large-scale hypertextual web search engine. Computer Networks and ISDN Systems, 30(1-7):107--117, 1998.

Digital Library

Google Scholar

[3]

G. Erkan and D. R. Radev. Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research, 22:457--479, 2004.

Digital Library

Google Scholar

[4]

B. Li and I. King. Routing questions to appropriate answerers in community question answering services. In CIKM'10, pages 1585--1588, 2010.

Digital Library

Google Scholar

[5]

A. S. M A Suryanto, Ee-Peng Lim and et al. Quality-aware collaborative question answering methods and evaluation. In WSDM'09, pages 142--152, 2009.

Digital Library

Google Scholar

[6]

K. Sun, Y. Cao, X. Song, and et al. Learning to recommend questions based on user ratings. In CIKM'09, pages 751--758, 2009.

Digital Library

Google Scholar

[7]

C. Zhai and J. Lafferty. A study of smoothing methods for language models applied to ad hoc information retrieval. In SIGIR'01, pages 334--342, 2001.

Digital Library

Google Scholar

Cited By

View all

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
Halder KKan MSugiyama KLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Health Forum Thread Recommendation Using an Interest Aware Topic ModelProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132946(1589-1598)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132946
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
Show More Cited By

Index Terms

Learning to recommend questions based on public interest
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking
    2. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

Recommendations

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 ...
Learning to recommend
Does Interactivity Serve the Public Interest?: The Role of Political Blogs in Deliberative Democracy

Political blogs are distinguished by their heightened interactivity, allowing users to participate directly in the political process. Does the interactivity afforded by political blogs really serve the public interest by contributing to deliberative ...

Comments

Information & Contributors

Information

Published In

CIKM '11: Proceedings of the 20th ACM international conference on Information and knowledge management

October 2011

2712 pages

ISBN:9781450307178

DOI:10.1145/2063576

Editors:
Bettina Berendt,
Arjen de Vries,
Wenfei Fan,
Craig Macdonald
University of Glasgow, UK
,
Iadh Ounis
University of Glasgow, UK
,
Ian Ruthven
University of Strathclyde, UK

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: 24 October 2011

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Poster

Conference

CIKM '11

Sponsor:

CIKM '11: International Conference on Information and Knowledge Management

October 24 - 28, 2011

Glasgow, Scotland, UK

Acceptance Rates

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

4
Total Citations
View Citations
254
Total Downloads

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

Reflects downloads up to 18 Feb 2025

Other Metrics

View Author Metrics

Citations

Cited By

View all

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
Halder KKan MSugiyama KLim EWinslett MSanderson MFu ASun JCulpepper SLo EHo JDonato DAgrawal RZheng YCastillo CSun ATseng VLi C(2017)Health Forum Thread Recommendation Using an Interest Aware Topic ModelProceedings of the 2017 ACM on Conference on Information and Knowledge Management10.1145/3132847.3132946(1589-1598)Online publication date: 6-Nov-2017
https://dl.acm.org/doi/10.1145/3132847.3132946
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
Huang YTang YLi CWu ZDong H(2015)A Method for Latent-Friendship Recommendation Based on Community Detection in Social NetworkProceedings of the 2015 12th Web Information System and Application Conference (WISA)10.1109/WISA.2015.16(3-8)Online publication date: 11-Sep-2015
https://dl.acm.org/doi/10.1109/WISA.2015.16

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.

PDF

eReader

View online with eReader.

eReader

Abstract

References

Cited By

Index Terms

Recommendations

Learning to recommend questions based on user ratings