Towards Psychometrics-based Friend Recommendations in Social Networking Services
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.
Keywords
Cite
@article{arxiv.1705.10512,
title = {Towards Psychometrics-based Friend Recommendations in Social Networking Services},
author = {Felix Beierle and Kai Grunert and Sebastian Göndör and Viktor Schlüter},
journal= {arXiv preprint arXiv:1705.10512},
year = {2017}
}
Comments
Accepted for publication at the 2017 International Conference on AI & Mobile Services (IEEE AIMS)