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العنوان
Towards trust-based social web /
الناشر
Ramy Ahmed Hanafy ,
المؤلف
Ramy Ahmed Hanafy
هيئة الاعداد
مشرف / Ramy Ahmed Hanafy
مشرف / Abeer Mohamed Elkorany
مشرف / Soha Makady
مناقش / Abeer Mohamed Elkorany
تاريخ النشر
2019
عدد الصفحات
91 Leaves :
اللغة
الإنجليزية
الدرجة
ماجستير
التخصص
Computer Science Applications
تاريخ الإجازة
21/10/2019
مكان الإجازة
جامعة القاهرة - كلية الحاسبات و المعلومات - Computer Science
الفهرس
Only 14 pages are availabe for public view

from 102

from 102

Abstract

Social networks have become one of the popular media for disseminating information and connecting people. Governments and enterprises have started exploiting these networks for delivering their services to citizens and customers. However, the success of such attempts relies on the level of trust that members have with each other as well as with the service provider. Trust has been described as a real component of any social relationship. Trust mainly refers to a measure of confidence on an entity that would behave in an expected manner, and has become an essential and important element of a successful social network. Academic social network (ASN) enables researchers to communicate and share publications. Traditional approaches to rank researchers are based on universal measures such as the H-index, which considers both the quality of researcher publications, and those publications{u2019} impact in one consolidated value. However, the H-index is highly affected by the duration of the researcher{u2019}s career, making it hard to compare and consequently rank researchers at different stages of their career, H-index also didn{u2019}t consider multiple characteristics (e.g. total number of publications, total work citation, total number of followers) in its calculation. This research aims to propose a model that utilizes the characteristics of researchers and their papers extracted from ASNs, in order to produce two scores, one that evaluates researcher and another to evaluate researcher{u2019}s scientific papers. Such scores are based on some extracted information that is prioritized according to user preferences. The proposed model recognizes both a researcher{u2019}s academic contribution and his influence on his fellow researchers in the form of an author score, and the influence of papers in the form of a paper score with the contribution of calculated author score