TY - JOUR
T1 - Voice of Charity
T2 - Prospecting the Donation Recurrence Donor Retention in Crowdfunding
AU - Zhao, Hongke
AU - Jin, Binbin
AU - Liu, Qi
AU - Ge, Yong
AU - Chen, Enhong
AU - Zhang, Xi
AU - Xu, Tong
N1 - Funding Information:
The projects are in the form of money-raising campaigns/loans (borrowers only need to repay the principal without any interest) posted by field partners for the needy or suffering. A project page contains the story and the information for funding use and repayment, the details about the funding and the clients (category, goal, duration, country, poverty, etc.). Some brief information about the field partner is also included in the project page.
Publisher Copyright:
© 1989-2012 IEEE.
PY - 2020/8/1
Y1 - 2020/8/1
N2 - Online donation-based crowdfunding has brought new life to charity by soliciting small monetary contributions from crowd donors to help others in trouble or with dreams. However, a crucial issue for crowdfunding platforms as well as traditional charities is the problem of high donor attrition, i.e., many donors donate only once or very few times within a rather short lifecycle and then leave. Thus, it is an urgent task to analyze the factors of and then further predict the donors behaviors. Especially, we focus on two types of behavioral events, e.g., donation recurrence (whether one donor will make donations at some time slices in the future) and donor retention (whether she will remain on the crowdfunding platform until a future time). However, this problem has not been well explored due to many domain and technical challenges, such as the heterogeneous influence, the relevance of the two types of events, and the censoring phenomenon of retention records. In this paper, we present a focused study on donation recurrence and donor retention with the help of large-scale behavioral data collected from crowdfunding. Specifically, we propose a Joint Deep Survival model, i.e., JDS, which can integrate heterogeneous features, e.g., donor motives, projects recently donated to, social contacts, to jointly model the donation recurrence and donor retention since these two types of behavioral events are highly relevant. In addition, we model the censoring phenomenon and dependence relations of different behaviors from the survival analysis view by designing multiple innovative constraints and incorporating them into the objective functions. Finally, we conduct extensive analysis and validation experiments with large-scale data collected from Kiva.org. The experimental results clearly demonstrate the effectiveness of our proposed models for analyzing and predicting the donation recurrence and donor retention in crowdfunding.
AB - Online donation-based crowdfunding has brought new life to charity by soliciting small monetary contributions from crowd donors to help others in trouble or with dreams. However, a crucial issue for crowdfunding platforms as well as traditional charities is the problem of high donor attrition, i.e., many donors donate only once or very few times within a rather short lifecycle and then leave. Thus, it is an urgent task to analyze the factors of and then further predict the donors behaviors. Especially, we focus on two types of behavioral events, e.g., donation recurrence (whether one donor will make donations at some time slices in the future) and donor retention (whether she will remain on the crowdfunding platform until a future time). However, this problem has not been well explored due to many domain and technical challenges, such as the heterogeneous influence, the relevance of the two types of events, and the censoring phenomenon of retention records. In this paper, we present a focused study on donation recurrence and donor retention with the help of large-scale behavioral data collected from crowdfunding. Specifically, we propose a Joint Deep Survival model, i.e., JDS, which can integrate heterogeneous features, e.g., donor motives, projects recently donated to, social contacts, to jointly model the donation recurrence and donor retention since these two types of behavioral events are highly relevant. In addition, we model the censoring phenomenon and dependence relations of different behaviors from the survival analysis view by designing multiple innovative constraints and incorporating them into the objective functions. Finally, we conduct extensive analysis and validation experiments with large-scale data collected from Kiva.org. The experimental results clearly demonstrate the effectiveness of our proposed models for analyzing and predicting the donation recurrence and donor retention in crowdfunding.
KW - Crowdfunding
KW - deep learning
KW - donor retention
KW - ranking constraints
KW - survival analysis
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UR - http://www.scopus.com/inward/citedby.url?scp=85088134778&partnerID=8YFLogxK
U2 - 10.1109/TKDE.2019.2906199
DO - 10.1109/TKDE.2019.2906199
M3 - Review article
AN - SCOPUS:85088134778
SN - 1041-4347
VL - 32
SP - 1652
EP - 1665
JO - IEEE Transactions on Knowledge and Data Engineering
JF - IEEE Transactions on Knowledge and Data Engineering
IS - 8
M1 - 8669972
ER -