# Towards Psychometrics-based Friend Recommendations in Social Networking Services

## TL;DR

This paper proposes using psychometric data derived from smartphone sensors to improve friend recommendations in social networks, enabling more distributed and privacy-aware systems beyond traditional graph-based methods.

## Contribution

It introduces a novel approach integrating psychometrics and sensor data for friend recommendations, moving beyond conventional social graph-based techniques.

## Key findings

- Psychometric traits can be inferred from smartphone sensor data.
- Psychometrics-based recommendations can complement existing social graph methods.
- Potential for more distributed and privacy-preserving social networking systems.

## Abstract

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. In this paper, 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.