Friendbook: A Semantic-Based Friend Recommendation System for Social Networks

TWENTY years past, folks generally created friends with others who live or work near themselves, like neighbors or colleagues. we have a tendency to decision friends created through this ancient fashion as G-friends, that stands for geographical location-based friends as a result of they're influenced by the geographical distances between one another. With the speedy advances in social networks, services like Facebook, Twitter and Google+ have provided us revolutionary ways in which of creating friends.According to Facebook statistics, a user has a mean of one hundred thirty friends, maybe larger than the other time in history. One challenge with existing social networking services is a way to suggest a good or reliable friend to a user. Most of them rely on pre-existing user relationships to choose friend candidates. for instance, Facebook depends on a social link analysis among those that already share common friends and recommends  users as potential friends.Unfortunately, this approach might not be the foremost applicable supported recent social science findings. according to these studies, the principles to group individuals along include: 1) habits or life style; 2) attitudes; 3) tastes; 4) ethical standards; 5) economic level; and 6) individuals they already know. life styles are typically closely correlate with daily routines and activities. Therefore, if we tend to may gather data on users’ daily routines and activities, we are able to exploit rule #1 and suggest friends to individuals supported their similar life styles. This recommendation mechanism may be deployed as a standalone app on smartphones for existing social network frameworks.