Paper Info
Title
PersonalitySensing: A Multi-View Multi-Task Learning Approach for Personality Detection based on Smartphone Usage
Abstract
Assessing individual's personality traits has important implications in psychology, sociology, and economics. Conventional personality measurement methods were questionnaire-based, which are time-consuming and manpower-expensive. With the pervasive deployment of mobile communication applications, smartphone usage data was found to relate to people's social behavioral and psychological aspects. In this paper, we propose a deep learning approach to infer people's Big Five personality traits based on smartphone data. Specifically, we collect smartphone usage snapshots with an Android App, and extract features from the collected data. We propose a multi-view multi-task learning approach with a deep neural network model to fuse the extracted features and learn the Big Five personality traits jointly. Extensive experiments based on the real-world smartphone data collected from university volunteers show that the proposed approach significantly outperforms the state-of-the-art algorithms in personality prediction.
Year
DOI
Venue
2020
10.1145/3394171.3413591
MM '20: The 28th ACM International Conference on Multimedia Seattle WA USA October, 2020
DocType
ISBN
Citations 
Conference
978-1-4503-7988-5
1
PageRank 
References 
Authors
0.34
10
6
Name
Order
Citations
PageRank
Songcheng Gao1121.48
Wenzhong Li267655.27
Lynda J. Song320.70
Xiao Zhang431.72
Mingkai Lin510.34
Sanglu Lu61380144.07