A New Approach to Answerer Recommendation in Community Question Answering Services

  • Zhenlei Yan
  • Jie Zhou
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)


Community Question Answering (CQA) service which enables users to ask and answer questions have emerged popular on the web. However, lots of questions usually can’t be resolved by appropriate answerers effectively. To address this problem, we present a novel approach to recommend users who are most likely to be able to answer the new question. Differently with previous methods, this approach utilizes the inherent semantic relations among asker-question-answerer simultaneously and perform the Answerer Recommendation task based on tensor factorization. Experimental results on two real-world CQA dataset show that the proposed method is able to recommend appropriate answerers for new questions and outperforms other state-of-the-art approaches.


answerer recommendation tensor factorization community question answering 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Zhenlei Yan
    • 1
  • Jie Zhou
    • 1
  1. 1.State Key Laboratory on Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of AutomationTsinghua UniversityBeijingChina

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