Abstract

Community Question Answering (CQA) sites, such as Stack Overflow and Yahoo! Answers, have become very popular in recent years. These sites contain rich crowdsourcing knowledge contributed by the site users in the form of questions and answers, and these questions and answers can satisfy the information needs of more users. In this article, we aim at predicting the voting scores of questions/answers shortly after they are posted in the CQA sites. To accomplish this task, we identify three key aspects that matter with the voting of a post, i.e., the non-linear relationships between features and output, the question and answer coupling, and the dynamic fashion of data arrivals. A family of algorithms are proposed to model the above three key aspects. Some approximations and extensions are also proposed to scale up the computation. We analyze the proposed algorithms in terms of optimality, correctness, and complexity. Extensive experimental evaluations conducted on two real data sets demonstrate the effectiveness and efficiency of our algorithms.

Original languageEnglish (US)
Article number7906587
Pages (from-to)1723-1736
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume29
Issue number8
DOIs
StatePublished - Aug 1 2017

Keywords

  • coupling
  • dynamics
  • non-linearity
  • Question answering
  • voting prediction

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

Cite this

Scalable Algorithms for CQA Post Voting Prediction. / Yao, Yuan; Tong, Hanghang; Xu, Feng; Lu, Jian.

In: IEEE Transactions on Knowledge and Data Engineering, Vol. 29, No. 8, 7906587, 01.08.2017, p. 1723-1736.

Research output: Contribution to journalArticle

Yao, Yuan ; Tong, Hanghang ; Xu, Feng ; Lu, Jian. / Scalable Algorithms for CQA Post Voting Prediction. In: IEEE Transactions on Knowledge and Data Engineering. 2017 ; Vol. 29, No. 8. pp. 1723-1736.
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