  Community Question Answering (CQA) websites have become valuable knowledge
repositories where individuals exchange information by asking and answering
questions. With an ever-increasing number of questions and high migration of
users in and out of communities, a key challenge is to design effective
strategies for recommending experts for new questions. In this paper, we
propose a simple graph-diffusion expert recommendation model for CQA, that can
outperform state-of-the art deep learning representatives and collaborative
models. Our proposed method learns users' expertise in the context of both
semantic and temporal information to capture their changing interest and
activity levels with time. Experiments on five real-world datasets from the
Stack Exchange network demonstrate that our approach outperforms competitive
baseline methods. Further, experiments on cold-start users (users with a
limited historical record) show our model achieves an average of ~ 30%
performance gain compared to the best baseline method.
