In online question-and-answer (QA) websites like Quora, one central issue is
to find (invite) users who are able to provide answers to a given question and
at the same time would be unlikely to say "no" to the invitation. The challenge
is how to trade off the matching degree between users' expertise and the
question topic, and the likelihood of positive response from the invited users.
In this paper, we formally formulate the problem and develop a weakly
supervised factor graph (WeakFG) model to address the problem. The model
explicitly captures expertise matching degree between questions and users. To
model the likelihood that an invited user is willing to answer a specific
question, we incorporate a set of correlations based on social identity theory
into the WeakFG model. We use two different genres of datasets: QA-Expert and
Paper-Reviewer, to validate the proposed model. Our experimental results show
that the proposed model can significantly outperform (+1.5-10.7% by MAP) the
state-of-the-art algorithms for matching users (experts) with community
questions. We have also developed an online system to further demonstrate the
advantages of the proposed method.

本研究旨在解决在线问答 (QA) 网站中寻找 (邀请) 能够回答给定问题且不太可能拒绝邀请的用户的问题。为此，我们提出了 WeakFG 模型以捕获问题和用户之间的专业匹配度，并将基于社会身份理论的一组相关性结合到模型中。我们使用两种不同类别的数据集：QA-Expert 和 Paper-Reviewer 来验证该模型的性能。实验结果表明，该模型可以比现有算法显著提高 (至少 + 1.5-10.7% by MAP) 用户与社区问题的匹配度。此外，我们还开发了一个在线系统来进一步展示所提出方法的优势。