Friend.ly: A pilot study for a personality-based friend recommendation system

Abstract

Social networks have become very popular in today’s age. As a result, Friend Recommendation Systems (FRSs) have gained immense importance. Teenagers today are often depressed and feel lonely. Social Networks, if used wisely can help a user connect to the right set of friends which can help to solve the above-mentioned challenges. Most popular social media platforms rely on homophily-based FRSs for their friend recommendations. That is, they use the user’s social graph to match him/her with other users. Users now are concerned about how their personal data is being used and its privacy. Therefore, most users do not reveal their personal information on social networks. Also, there is no evidence to the accuracy of user information. Therefore, the existing recommendation systems in social media prove to be inaccurate in a lot of cases. Furthermore, the main purpose of these traditional recommendation systems is to connect pre-existing friends online rather than suggesting users that could be potential friends. To connect users to a new set of people, there is a need to design a friend recommendation system that matches users based on something other than existing connections. In this paper, we propose a system which uses personality analysis along with hybrid filtering to make personalized friend recommendations along with other filtering options for age range, location, gender, etc. This system would solve the cold start problem which occurs because in the present scenario people don’t like spending time to specify their preferences for friend recommendations. The filtering options are of further interest to the user as there is a possibility of connecting to a new bunch of friends in person too.