The importance of social networking sites (SNS) in our life is increasing day by day as they are attracting millions of users by their interesting features and activities. Joining different communities is one of the most common activities of users in social network. However, information overloading has troubled many users as thousands of communities are being created each day. To solve this problem, we have introduced a cohesion based community recommendation system where cohesion means high degree of connection among SNS users. Our proposed framework consists of the steps like, extracting sub-network (e.g. Facebook), measuring the friendship factors (both offline and online), measuring user preference factor, calculating threshold from present communities of any user, and finally recommending community based on automatically derived threshold.
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Cohesion based personalized community recommendation system
Semantic Scholar · Computer Science · 2015
Abstract
The importance of social networking sites (SNS) in our life is increasing day by day as they are attracting millions of users by their interesting features and activities. Joining different communities is one of the most common activities of users in social network. However, information overloading has troubled many users as thousands of communities are being created each day. To solve this problem, we have introduced a cohesion based community recommendation system where cohesion means high degree of connection among SNS users. Our proposed framework consists of the steps like, extracting sub-network (e.g. Facebook), measuring the friendship factors (both offline and online), measuring user preference factor, calculating threshold from present communities of any user, and finally recommending community based on automatically derived threshold.