Question-and-answering (Q&A) sites are information systems that allow users to ask and answer questions. Users can learn by frequently discussing, answering questions, or exchanging opinions with other experts using Q&A systems. In addition, they can arrange the existing top answers using a number of upvotes and downvotes from experts and crowd wisdom. The number of knowledge-sharing sites has increased significantly in recent years. However, some Q&A sites began to shrink (Yahoo Answers) or were shut down (Google Answers). The main reason is low-quality answers because they do not connect visitors and experts with the right questions. In addition, a question may contain several subtopics with which the expert is unfamiliar. The recommendation of a list of experts closest to the question will lead to a long-tail problem. In this paper, we propose an expert group recommendation method for Q&A systems by taking into consideration users’ behaviors and diversity criteria in the group. Users’ behavior is analyzed to determine a group of experts or non-experts on specific topics. Diversity is an important factor in promoting the sustained comprehensible growth of Q&A sites and avoid following the crowd. Experiments on a Quora dataset show that our method achieves better results in terms of accuracy in comparison with other methods.
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