  The rapid development recently of Community Question Answering (CQA)
satisfies users quest for professional and personal knowledge about anything.
In CQA, one central issue is to find users with expertise and willingness to
answer the given questions. Expert finding in CQA often exhibits very different
challenges compared to traditional methods. Sparse data and new features
violate fundamental assumptions of traditional recommendation systems. This
paper focuses on reviewing and categorizing the current progress on expert
finding in CQA. We classify all the existing solutions into four different
categories: matrix factorization based models (MF-based models), gradient
boosting tree based models (GBT-based models), deep learning based models
(DL-based models) and ranking based models (R-based models). We find that
MF-based models outperform other categories of models in the field of expert
finding in CQA. Moreover, we use innovative diagrams to clarify several
important concepts of ensemble learning, and find that ensemble models with
several specific single models can further boosting the performance. Further,
we compare the performance of different models on different types of matching
tasks, including text vs. text, graph vs. text, audio vs. text and video vs.
text. The results can help the model selection of expert finding in practice.
Finally, we explore some potential future issues in expert finding research in
CQA.
