Friendbook: Based on Lifestyle of Users with Unwanted Message Fltering

Abstract

Normally social networking services recommend
friends to users based on their social Graphs, which may
not be the most appropriate/correct to recommend a users
friend preferences for friendselection in reality. In this system
a novel semantic based friend recommendation system for social
networks, which recommends friends to users based on their life
styles instead of social graphs. Friendbook discovers life styles of
users from user-centric data, measures the similarity of life styles
between users, and recommends friends to users if their life styles
have high similarity. We uses a similarity metric to measure the
similarity of life styles between users and also calculate users
impact in terms of life styles with a friend-matching graph.
Once user received a request, Friendbook returns a list of people
which is having highest recommendation scores to the query user.
Further it improves the recommendation accuracy by integrating
a feedback by using feedback mechanism. These results shows
that the recommendations are accurately reflect the preferences
of users in choosing friends. This system also filters the unwanted
message from social networks to control the crime by using
message filtering mechanism.