A Recommendation System for Finding Experts in Online Scientific Communities

Online scientific communities are the bases that publish books, journals, and scientific papers, and help promote the knowledge. the researchers use the search engines in order to find the given information including scientific papers, an expert to collaborate with, and the publication venue, but in many cases, due to the search by keywords and lack of attention to the content, they do not achieve the desired results at the early stages. online scientific communities can increase the system efficiency to respond to their users utilizing a customized search. in this paper, using a dataset including bibliographic information of the user’s publication, the publication venues, and other published papers provide a way to find an expert in a particular context, where the experts are recommended to a user according to his/her records and preferences. in this way, a user request to find an expert is presented with the keywords that represent a certain expertise, and the system output will be a certain number of ranked suggestions for a specific user. each suggestion is the name of an expert who has been identified appropriate to collaborate with the user. in evaluation using the ieee database, the proposed method reaches an accuracy of 71.50% that seems to be an acceptable result.