Abstract

Community Question Answering (CQA) sites have become valuable repositories that host a massive volume of human knowledge. How can we detect a high-value answer which clears the doubts of many users? Can we tell the user if the question s/he is posting would attract a good answer? In this paper, we aim to answer these questions from the perspective of the voting outcome by the site users. Our key observation is that the voting score of an answer is strongly positively correlated with that of its question, and such correlation could be in turn used to boost the prediction performance. Armed with this observation, we propose a family of algorithms to jointly predict the voting scores of questions and answers soon after they are posted in the CQA sites. Experimental evaluations demonstrate the effectiveness of our approaches.

Original languageEnglish (US)
Title of host publicationASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
EditorsMartin Ester, Guandong Xu, Xindong Wu, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages340-343
Number of pages4
ISBN (Electronic)9781479958771
DOIs
StatePublished - Oct 10 2014
Event2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014 - Beijing, China
Duration: Aug 17 2014Aug 20 2014

Publication series

NameASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining

Other

Other2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2014
CountryChina
CityBeijing
Period8/17/148/20/14

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

Cite this

Yao, Y., Tong, H., Xie, T., Akoglu, L., Xu, F., & Lu, J. (2014). Joint voting prediction for questions and answers in CQA. In M. Ester, G. Xu, X. Wu, & X. Wu (Eds.), ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 340-343). [6921607] (ASONAM 2014 - Proceedings of the 2014 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ASONAM.2014.6921607