Personal credit profiling via latent user behavior dimensions on social media

Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, exi...

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Main Authors: GUO, Guangming, ZHU, Feida, CHEN, Enhong, WU, Le, LIU, Qi, LIU, Yingling, QIU, Minghui
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Language:English
Published: Institutional Knowledge at Singapore Management University 2016
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Online Access:https://ink.library.smu.edu.sg/sis_research/3607
https://ink.library.smu.edu.sg/context/sis_research/article/4608/viewcontent/Personal_credit_profiling_via_latent_user_behavior_dimensions_on_social_media.pdf
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spelling sg-smu-ink.sis_research-46082017-04-10T08:28:57Z Personal credit profiling via latent user behavior dimensions on social media GUO, Guangming ZHU, Feida CHEN, Enhong WU, Le LIU, Qi LIU, Yingling QIU, Minghui Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal credit profiling. LUBD-CM learns users’ hidden behavior habits and topic distributions simultaneously, and represents each user at a much finer granularity. Specifically, we take a real-world Sina Weibo dataset as the testbed for personal credit profiling evaluation. Experiments conducted on the dataset demonstrate the effectiveness of our approach: (1) User credit label can be predicted using LUBD-CM with a considerable performance improvement over state-of-the-art baselines; (2) The latent behavior dimensions have very good interpretability in personal credit profiling. 2016-04-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/3607 info:doi/10.1007/978-3-319-31750-2_11 https://ink.library.smu.edu.sg/context/sis_research/article/4608/viewcontent/Personal_credit_profiling_via_latent_user_behavior_dimensions_on_social_media.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Social networking Artificial Intelligence and Robotics Databases and Information Systems
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Social networking
Artificial Intelligence and Robotics
Databases and Information Systems
spellingShingle Social networking
Artificial Intelligence and Robotics
Databases and Information Systems
GUO, Guangming
ZHU, Feida
CHEN, Enhong
WU, Le
LIU, Qi
LIU, Yingling
QIU, Minghui
Personal credit profiling via latent user behavior dimensions on social media
description Consumer credit scoring and credit risk management have been the core research problem in financial industry for decades. In this paper, we target at inferring this particular user attribute called credit, i.e., whether a user is of the good credit class or not, from online social data. However, existing credit scoring methods, mainly relying on financial data, face severe challenges when tackling the heterogeneous social data. Moreover, social data only contains extremely weak signals about users’ credit label. To that end, we put forward a Latent User Behavior Dimension based Credit Model (LUBD-CM) to capture these small signals for personal credit profiling. LUBD-CM learns users’ hidden behavior habits and topic distributions simultaneously, and represents each user at a much finer granularity. Specifically, we take a real-world Sina Weibo dataset as the testbed for personal credit profiling evaluation. Experiments conducted on the dataset demonstrate the effectiveness of our approach: (1) User credit label can be predicted using LUBD-CM with a considerable performance improvement over state-of-the-art baselines; (2) The latent behavior dimensions have very good interpretability in personal credit profiling.
format text
author GUO, Guangming
ZHU, Feida
CHEN, Enhong
WU, Le
LIU, Qi
LIU, Yingling
QIU, Minghui
author_facet GUO, Guangming
ZHU, Feida
CHEN, Enhong
WU, Le
LIU, Qi
LIU, Yingling
QIU, Minghui
author_sort GUO, Guangming
title Personal credit profiling via latent user behavior dimensions on social media
title_short Personal credit profiling via latent user behavior dimensions on social media
title_full Personal credit profiling via latent user behavior dimensions on social media
title_fullStr Personal credit profiling via latent user behavior dimensions on social media
title_full_unstemmed Personal credit profiling via latent user behavior dimensions on social media
title_sort personal credit profiling via latent user behavior dimensions on social media
publisher Institutional Knowledge at Singapore Management University
publishDate 2016
url https://ink.library.smu.edu.sg/sis_research/3607
https://ink.library.smu.edu.sg/context/sis_research/article/4608/viewcontent/Personal_credit_profiling_via_latent_user_behavior_dimensions_on_social_media.pdf
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