  Community question answering (CQA) represents the type of Web applications
where people can exchange knowledge via asking and answering questions. One
significant challenge of most real-world CQA systems is the lack of effective
matching between questions and the potential good answerers, which adversely
affects the efficient knowledge acquisition and circulation. On the one hand, a
requester might experience many low-quality answers without receiving a quality
response in a brief time, on the other hand, an answerer might face numerous
new questions without being able to identify their questions of interest
quickly. Under this situation, expert recommendation emerges as a promising
technique to address the above issues. Instead of passively waiting for users
to browse and find their questions of interest, an expert recommendation method
raises the attention of users to the appropriate questions actively and
promptly. The past few years have witnessed considerable efforts that address
the expert recommendation problem from different perspectives. These methods
all have their issues that need to be resolved before the advantages of expert
recommendation can be fully embraced. In this survey, we first present an
overview of the research efforts and state-of-the-art techniques for the expert
recommendation in CQA. We next summarize and compare the existing methods
concerning their advantages and shortcomings, followed by discussing the open
issues and future research directions.
