Towards Psychometrics-based Friend Recommendations in Social Networking Services

Two of the defining elements of Social Networking Services are the social profile, containing information about the user, and the social graph, containing information about the connections between users. Social Networking Services are used to connect to known people as well as to discover new contacts. Current friend recommendation mechanisms typically utilize the social graph. We argue that psychometrics, the field of measuring personality traits, can help make meaningful friend recommendations based on an extended social profile containing collected smartphone sensor data. This will support the development of highly distributed Social Networking Services without central knowledge of the social graph.

We presented this work as a paper at the IEEE International Conference on AI & Mobile Services (AIMS) 2017. You can find the full paper here (PDF).

Screenshots of our Android prototype.
The core idea of our concept: mobile sensing collects data that implies Big Five personality traits, which, in turn, can be used for recommending new connections.