Weakly Learning to Match Experts in Online Community
  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.
