شماره ركورد كنفرانس :
4753
عنوان مقاله :
Identifying and comparing features and facilities of scienti c social networks for recommending collaborators
عنوان به زبان ديگر :
Identifying and comparing features and facilities of scienti c social networks for recommending collaborators
پديدآورندگان :
Roozbahani Zahra z.roozbahani@stu.qom.ac.ir University of Qom, Qom, Iran , Rezaeenour Jalal j.rezaee@qom.ac.ir University of Qom, Qom, Iran , Emamgholizadeh Hanif 3 Department of Computer Science, Yazd University, Yazd, Iran , Belkin Markus School of Medicine, Faculty of Health, Deakin University, Burwood, Australia
كليدواژه :
Social networks Recommending systems Collaborator recommendation Expertise nding
عنوان كنفرانس :
اولين كنفرانس بين المللي محاسبات و سامانه هاي توزيع شده
چكيده فارسي :
Social networks are now an inseparable part of our life, each of us use social network for a special purposes from social interaction to marketing. One of the ourishing aspects of social networks is scienti c social networks; users of these networks try to make public pro les, attach publications there, ask their questions and nd new collaborators for future work. Having been considered for the last several decades in the data management eld, recommending systems has also attracted a great deal of attention in computer science, and after the emergence of on-line social networks collaboration, suggestions for its use became an inseparable dimension of these young networks. In this paper some of the most popular and creative social networks have been considered, all of the useful features have been identi ed and compared, and nally the limitations of considered systems in providing direct collaborator recommendation has been discussed.
چكيده لاتين :
Social networks are now an inseparable part of our life, each of us use social network for a special purposes from social interaction to marketing. One of the ourishing aspects of social networks is scienti c social networks; users of these networks try to make public pro les, attach publications there, ask their questions and nd new collaborators for future work. Having been considered for the last several decades in the data management eld, recommending systems has also attracted a great deal of attention in computer science, and after the emergence of on-line social networks collaboration, suggestions for its use became an inseparable dimension of these young networks. In this paper some of the most popular and creative social networks have been considered, all of the useful features have been identi ed and compared, and nally the limitations of considered systems in providing direct collaborator recommendation has been discussed.
