Who should I add as a "friend"?: a study of friend recommendations using proximity and homophily

Abstract

We receive many recommendations of friends in online social networks such as Facebook and LinkedIn. These friend recommendations are based usually on common friends or similar profile such as having the same interest or coming from the same company, a trait known as homophily. However, many times people do not know why they should add this friend. Should I add this friend because we met from a conference and if so, what conference? Existing friend recommendation systems cannot answer this question easily. In this paper, we create a friend recommendation system using proximity and homophily, that we conduct in the workplace and conference. Besides common friends and common interests (homophily features), we also include encounters and meetings (proximity features) and messages sent and question and answer posts (social interaction features) as reasons for adding this person as a friend. We conduct a user study to examine whether our friend recommendation is better than common friends. Results show that on average, our algorithm recommends more friends to participants that they add and more recommendations are ranked as good, compared with the common friend algorithm. In addition, people add friends due to having encountered them before in real life. The results can be used to help design context-aware recommendations in physical environments and in online social networks.