TY - GEN
T1 - Personality Traits Prediction Based on Sparse Digital Footprints via Discriminative Matrix Factorization
AU - Wang, Shipeng
AU - Zhang, Daokun
AU - Cui, Lizhen
AU - Lu, Xudong
AU - Liu, Lei
AU - Li, Qingzhong
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Identifying individuals’ personality traits from their digital footprints has been proved able to improve the service of online platforms. However, due to the privacy concerns and legal restrictions, only some sparse, incomplete and anonymous digital footprints can be accessed, which seriously challenges the existing personality traits identification methods. To make the best of the available sparse digital footprints, we propose a novel personality traits prediction algorithm through jointly learning discriminative latent features for individuals and a personality traits predictor performed on the learned features. By formulating a discriminative matrix factorization problem, we seamlessly integrate the discriminative individual feature learning and personality traits predictor learning together. To solve the discriminative matrix factorization problem, we develop an alternative optimization based solution, which is efficient and easy to be parallelized for large-scale data. Experiments are conducted on the real-world Facebook like digital footprints. The results show that the proposed algorithm outperforms the state-of-the-art personality traits prediction methods significantly.
AB - Identifying individuals’ personality traits from their digital footprints has been proved able to improve the service of online platforms. However, due to the privacy concerns and legal restrictions, only some sparse, incomplete and anonymous digital footprints can be accessed, which seriously challenges the existing personality traits identification methods. To make the best of the available sparse digital footprints, we propose a novel personality traits prediction algorithm through jointly learning discriminative latent features for individuals and a personality traits predictor performed on the learned features. By formulating a discriminative matrix factorization problem, we seamlessly integrate the discriminative individual feature learning and personality traits predictor learning together. To solve the discriminative matrix factorization problem, we develop an alternative optimization based solution, which is efficient and easy to be parallelized for large-scale data. Experiments are conducted on the real-world Facebook like digital footprints. The results show that the proposed algorithm outperforms the state-of-the-art personality traits prediction methods significantly.
KW - Alternative optimization
KW - Digital footprints
KW - Discriminative matrix factorization
KW - Personality traits prediction
UR - http://www.scopus.com/inward/record.url?scp=85104802810&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-73197-7_47
DO - 10.1007/978-3-030-73197-7_47
M3 - Conference contribution
AN - SCOPUS:85104802810
SN - 9783030731960
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 692
EP - 700
BT - Database Systems for Advanced Applications - 26th International Conference, DASFAA 2021, Proceedings
A2 - Jensen, Christian S.
A2 - Lim, Ee-Peng
A2 - Yang, De-Nian
A2 - Chang, Chia-Hui
A2 - Xu, Jianliang
A2 - Peng, Wen-Chih
A2 - Huang, Jen-Wei
A2 - Shen, Chih-Ya
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Database Systems for Advanced Applications, DASFAA 2021
Y2 - 11 April 2021 through 14 April 2021
ER -