Title: Modelling user pictures with hierarchical Dirichlet process of P2P lending market

Authors: Danyang Li; Yongquan Liang; An Liu

Addresses: College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China; Department of Computer Science, Michigan Technological University, Houghton, 49931 MI, USA ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China Provincial Key Lab for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao 266590, China ' College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China Provincial Key Lab for Information Technology of Wisdom Mining of Shandong Province, Shandong University of Science and Technology, Qingdao 266590, China

Abstract: The emergence of peer-to-peer (P2P) lending has drawn a lot of attention. The enormous data generated from this billions level market bring us a lots of challenges and opportunities. One interesting question of modelling this data is that can we discover the hidden pattern of users' characteristics from it? Currently, few works have been made to this area. In this article, we try to build a Bayesian probabilistic model to discover the latent user pictures. Especially, we build a user picture model via hierarchical Dirichlet process from the data of one of the biggest market, lending club. The discovered user picture is interpretable and can be evaluated from many perspectives. To demonstrate the usage of user picture, we also proposed a method to predict the loan status. The experimental results show our approach outperformed the comparison methods.

Keywords: peer-to-peer; P2P lending; user pictures; hierarchical Dirichlet process; HDP; prediction.

DOI: 10.1504/IJCSM.2019.10022398

International Journal of Computing Science and Mathematics, 2019 Vol.10 No.3, pp.297 - 312

Received: 02 Oct 2016
Accepted: 19 Jul 2017

Published online: 24 Jul 2019 *

Full-text access for editors Access for subscribers Purchase this article Comment on this article