scholarly journals The Factors of Forming Social Capital in Social Network Services and the Relationship between Social Capital and the Propensity of Consumer Needs

2014 ◽  
Vol 17 (3) ◽  
pp. 105-130
Author(s):  
이희태
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Network Services ◽  
Social Network Services ◽  
The Relationship

Social network services for academic libraries: A study based on social capital and social proof

2020 ◽  
Vol 46 (1) ◽  
pp. 102091 ◽  
Author(s):  
Kenny Cheuk Hei Fong ◽  
Cheuk Hang Au ◽  
Ernest Tak Hei Lam ◽  
Dickson K.W. Chiu
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Academic Libraries ◽  
Network Services ◽  
Social Proof ◽  
Social Network Services

The Influence of Cultural Differences on the Use of Social Network Services and the Formation of Social Capital

2010 ◽  
Vol 26 (11-12) ◽  
pp. 1100-1121 ◽  
Author(s):  
Yong Gu Ji ◽  
Hwan Hwangbo ◽  
Ji Soo Yi ◽  
P. L. Patrick Rau ◽  
Xiaowen Fang ◽  
...  
Keyword(s):  
Social Capital ◽  
Social Network ◽  
Cultural Differences ◽  
Network Services ◽  
Social Network Services

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.

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