Friend Recommendation System for the Social Network based on User Behavior

Abstract

Social network sites have connected millions of users creating the social revolution in Web 2.0 now-a-days. If the group of people or organizations have the common interest then, a social network is constituted. In the present world, the most visited sites in the Internet are Twitter, Facebook, Orkut, Google plus etc. which is actually Online social networking sites. In the social network sites, a user makes friends with the other users and enjoy the communication with them. However, the large amount of online users and their diverse and dynamic interests possess great challenges to support such a novel feature in online social networks. In this thesis, we design a general friend recommendation framework based on user behavior. The main idea of the proposed method is consisted of the following stages- measuring the frequency of the activities and updating the dataset according to the activities, applying FP-Growth algorithm to find out the user behavior, then finding out the uncommon behavior containing the common behavior.