Expert2Vec: Experts Representation in Community Question Answering for Question Routing

  • Sara MumtazEmail author
  • Carlos Rodriguez
  • Boualem Benatallah
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)


Communities of Question Answering (CQAs) are rapidly growing communities for exchanging information in the form of questions and answers. They rely on the contributions of users (i.e., members of the community) who have appropriate domain knowledge and can provide helpful answers. In order to deliver the most appropriate and valuable answers, identification of such users (experts) is critically important. However, a common problem faced in CQAs is that of poor expertise matching, i.e., routing of questions to inappropriate users. In this paper, we focus on Stack Overflow (a programming CQA) and address this problem by proposing an embedding based approach that integrates users’ textual content obtained from the community (e.g., answers) and community feedback in a unified framework. Our embedding-based approach is used to find the best relevant users for a given question by computing the similarity between questions and our user expertise representation. Then, our framework exploits feedback from the community to rank the relevant users according to their expertise. We experimentally evaluate the performance of the proposed approach using Stack Overflow’s dataset, compare it with state-of-the-art models and demonstrate that it can produce better results than the alternative models.


Experts finding Question and answering communities Embeddings models Stack overflow Expert representation 



The work of the second and third authors is supported by Data to Decisions CRC (D2D-CRC).


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sara Mumtaz
    • 1
    Email author
  • Carlos Rodriguez
    • 1
  • Boualem Benatallah
    • 1
  1. 1.School of Computer Science and EngineeringUNSW SydneySydneyAustralia

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