A systematic survey on collaborator finding systems in scientific social networks
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A systematic survey on collaborator finding systems in scientific social networks Zahra Roozbahani1 · Jalal Rezaeenour2 · Hanif Emamgholizadeh3 · Amir Jalaly Bidgoly1 Received: 17 February 2019 / Revised: 12 June 2020 / Accepted: 13 June 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract 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. Keywords Social networks · Collaborator fining · Expert finding
1 Introduction Nowadays, existence of a huge amount of information has arisen as a big problem in determining or identifying the collaborators in a special topic. Expert recommendation as a branch of knowledge management science tries to reduce density problem of information and present a list of experts in the user’s requested topics. In the past, expert recommendation was confined to an organization, in which there existed knowledge repository and existing data were
Zahra Roozbahani and Jalal Rezaeenour authors have contributed in the paper equally.
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Jalal Rezaeenour [email protected]
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Department of Computer Engineering and IT, University of Qom, Qom, Iran
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Department of Industrial Engineering, University of Qom, Qom, Iran
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Department of Computer Science, Yazd University, Yazd, Iran
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Z. Roozbahani et al.
registered in a structured form and contained information with high quality. De Meo et al. [32] and Rachiling and Wulf [87] developed expert finding systems to a commercial and industrial scale. Progressive usage of Internet has made difficult utilizing of the available data. Today, online social networks as a subset of social media have attracted a great number of users, in which they produce and share contents and also have relation with each other. In this circumstance, it is important to produce a system able to look up required information based on the features of each user in the huge amount of available information. Therefore, recommender systems have attra
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