Quality of experience of VoIP for social network services: Facebook vs LINE over 3G networks in North Bangkok

2014 ◽  
Author(s):  
Pongpisit Wuttidittachotti ◽  
Kiattisak Yochanang ◽  
Narumon Chumkot ◽  
Tuul Triyason ◽  
Therdpong Daengsi
Keyword(s):  
Social Network ◽  
Quality Of Experience ◽  
Network Services ◽  
3G Networks ◽  
Social Network Services

scholarly journals Factors influencing students’ satisfaction and its impact on their loyalty for the University: Case Study at The University of Jordan-Aqaba Branch

2022 ◽  
Vol 11 (1) ◽  
pp. 1-18
Author(s):  
Prof. Dr. Ra’ed Masa’deh ◽  
◽  
Maram Omar Alsmadi ◽  
Ahed Mostafa Ameen Alsmadi ◽  
Ala'a Ziad Zayyad ◽  
...  
Keyword(s):  
Social Network ◽  
Positive Impact ◽  
Network Services ◽  
Measurement Tool ◽  
Social Network Services ◽  
University Of Jordan ◽  
The University ◽  
The Impact ◽  
Students Satisfaction

This study aimed to measure the impact of several antecedent factors on student’s satisfaction (i.e., academic aspects, non-academic aspects, program issues, reputation, access, quality of university facility, university location and social network services) and the mediator factor of students’ satisfactions' impact on student’s loyalty on the university of Jordan-Aqaba Brunch. Measurement tool was developed to examine the relationship between the study variables. The sample of 379 was used from the university of Jordan-Aqaba brunch students. Results indicated that academic aspects, non-academic aspects, reputation, university location and social network services impacted students' satisfaction directly. In the other hand, program issues, access and quality of university facility did not impact students' satisfaction. However, there was a positive impact of students' satisfaction on students' loyalty.

scholarly journals An Information Recommendation Technique Based on Influence and Activeness of Users in Social Networks

2021 ◽  
Vol 11 (6) ◽  
pp. 2530
Author(s):  
Minsoo Lee ◽  
Soyeon Oh
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Recommendation Systems ◽  
Network Services ◽  
The Past ◽  
User Behaviors ◽  
User Influence ◽  
User Activity ◽  
Information Recommendation ◽  
Social Network Services

Over the past few years, the number of users of social network services has been exponentially increasing and it is now a natural source of data that can be used by recommendation systems to provide important services to humans by analyzing applicable data and providing personalized information to users. In this paper, we propose an information recommendation technique that enables smart recommendations based on two specific types of analysis on user behaviors, such as the user influence and user activity. The components to measure the user influence and user activity are identified. The accuracy of the information recommendation is verified using Yelp data and shows significantly promising results that could create smarter information recommendation systems.

Face hallucination and recognition in social network services

2014 ◽  
Vol 71 (6) ◽  
pp. 2035-2049 ◽  
Author(s):  
Feng Jiang ◽  
Seungmin Rho ◽  
Bo-Wei Chen ◽  
Xiaodan Du ◽  
Debin Zhao
Keyword(s):  
Social Network ◽  
Network Services ◽  
Face Hallucination ◽  
Social Network Services
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