TY - GEN
T1 - Leveraging implicit contribution amounts to facilitate microfinancing requests
AU - Ranganath, Suhas
AU - Beigi, Ghazaleh
AU - Liu, Huan
N1 - Funding Information:
This material is based upon work supported by, or in part by, Office of Naval Research (ONR) under grant numbers N000141410095 and N00014-16-1-2257.
Publisher Copyright:
© 2018 Association for Computing Machinery.
PY - 2018/2/2
Y1 - 2018/2/2
N2 - The emergence of online microfinancing platforms provides new opportunities for people to seek financial assistance from a large number of potential contributors. However, these platforms deal with a huge number of requests, making it hard for the requesters to get assistance for their financial needs. Designing algorithms to identify potential contributors for a given request will assist in satisfying financial needs of requesters and improve the effectiveness of microfinancing platforms. Existing work correlates requests with contributor interests and profiles to design feature based approaches for recommending projects to prospective contributors. However, contributing money to financial requests has a cost on contributors which can affect his inclination to contribute in the future. Literature in economic behavior has investigated the manner in which memory of past contribution amounts affects user inclination to contribute to a given request. To systematically investigate whether these characteristics of economic behavior would help to facilitate requests in online microfinancing platforms, we present a novel framework to identify contributors for a given request from their past financial information. Individual contribution amounts are not publicly available, so we draw from financial modeling literature to model the implicit contribution amounts made to past requests. We evaluate the framework on two microfinancing platforms to demonstrate its effectiveness in identifying contributors.
AB - The emergence of online microfinancing platforms provides new opportunities for people to seek financial assistance from a large number of potential contributors. However, these platforms deal with a huge number of requests, making it hard for the requesters to get assistance for their financial needs. Designing algorithms to identify potential contributors for a given request will assist in satisfying financial needs of requesters and improve the effectiveness of microfinancing platforms. Existing work correlates requests with contributor interests and profiles to design feature based approaches for recommending projects to prospective contributors. However, contributing money to financial requests has a cost on contributors which can affect his inclination to contribute in the future. Literature in economic behavior has investigated the manner in which memory of past contribution amounts affects user inclination to contribute to a given request. To systematically investigate whether these characteristics of economic behavior would help to facilitate requests in online microfinancing platforms, we present a novel framework to identify contributors for a given request from their past financial information. Individual contribution amounts are not publicly available, so we draw from financial modeling literature to model the implicit contribution amounts made to past requests. We evaluate the framework on two microfinancing platforms to demonstrate its effectiveness in identifying contributors.
KW - Crowdfunding
KW - Information seeking
KW - Q&A
KW - Socioeconmics
UR - http://www.scopus.com/inward/record.url?scp=85046906138&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85046906138&partnerID=8YFLogxK
U2 - 10.1145/3159652.3159679
DO - 10.1145/3159652.3159679
M3 - Conference contribution
AN - SCOPUS:85046906138
T3 - WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
SP - 477
EP - 485
BT - WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery, Inc
T2 - 11th ACM International Conference on Web Search and Data Mining, WSDM 2018
Y2 - 5 February 2018 through 9 February 2018
ER -