Friend Finder: A Lifestyle based Friend Recommender App for Smart Phone Users

Abstract

Today’s 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 using the users’ link analysis may not be the best preference of suggestion for the users. In this paper, we present FriendFinder, a reliable user relation based friend suggesting app which recommends friend list to app users based on their analysis of 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.