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.
Highlights
With the advent of Web 2.0, social networking sites are becoming more widespread and interactive
Apposite groups or communities are suggested by an efficacious community or group recommendation system to a particular user so that user feels confident enough to join those suggested communities or groups
Our proposed community or group recommendation system is based on user proclivity and user actives, liveliness or interaction in social networking sites
Summary
With the advent of Web 2.0, social networking sites are becoming more widespread and interactive. The more cohesive group of community or group has higher linked strength measured in terms of three factors: amity factors, user proclivity rank, community preferences We have defined these terms in our proposed recommendation system to suggest the user effective communities or groups to join rather than the irrelevant ones. An previous group of study in this field focused on recommending groups or communities or groups on basis of user profile contents or Homophily [21] (user similarity) They do not ponder over degree of interaction among users or combinational impact of various factors like user preference and amity impact. The major contributions of the paper are as follows: (1) We apply user proclivity factor to approximate user’s personal interest over groups or communities or groups in social network. The initial research of this paper was published in the 18th International Conference www.ijacsa.thesai.org (IJACSA) International Journal of Advanced Computer Science and Applications, Vol 7, No 8, 2016 on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, held on 21-23 December, 2015 [1]
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