A Survey Paper on FriendFinder: A Lifestyle based Friend Recommender App for Smart Phone Users

Todays Social Networking services focuses towards suggesting you friends based on users social graph or Geo-location based, which neither take users life style into account or users liking, disliking etc. Suggesting friends based on social graphs may not be the best preference for the users. In this paper, we present FriendFinder, a novel semantic-based friend suggesting system which suggest friends to users based on their life style and daily curricular activities on mobile phone instead of social graphs. FriendFinder captures users data i. e. daily activities and work done through mobile, for ex - App Usage, App Frequency, Browser Activities etc. Then we create a user profile with all gathered data and find most relevant matching profiles of existing candidate friends matching our profile for similarity and suggesting the result out of similarity test to the user as a friend.