The increasing number of researchers and scientists participating in online communities has induced big challenges for users who are looking for researchers who are interested. As a result, finding potential collaborators among the huge amount of online information is going to be even much more important in the future. Collaborator recommendation is a kind of expert recommendation in scientific fields. A number of published papers have proposed new algorithms for an expert or a collaborator finding and tacking a narrower point of view. For instance, some of these papers have particularly considered a collaborator finding problem. New scientific social networks, such as ResearchGate, Academia, Mendeley, and so on, have provided some facilities to their users for finding new collaborators. In this paper, first of all, we review proposed models for an expert and a collaborator finding in scientific and academic social networks in a systematic manner. Next, collaborator finding facilities in online scientific social networks are evaluated. Finally, the defects and open challenges of the models are looked into and some propositions for the future works are presented.

参与在线社区的研究人员和科学家的数量不断增加，这对正在寻找感兴趣的研究人员的用户造成了巨大挑战。结果，将来在大量的在线信息中寻找潜在的合作者将变得更加重要。合作者推荐是科学领域的一种专家推荐。许多发表的论文提出了一种新的算法，用于专家或协作者寻找并解决狭窄的观点。例如，其中一些论文特别考虑了协作者发现问题。新的科学社交网络，例如ResearchGate，Academia，Mendeley等，已经为用户提供了一些寻找新合作者的便利。在本文中，首先，我们以系统的方式回顾了在科学和学术社交网络中为专家和协作者发现的建议模型。接下来，评估在线科学社交网络中的协作者查找设施。最后，研究了模型的缺陷和开放性挑战，并对未来的工作提出了一些建议。