Show simple item record

dc.contributor.authorChoo, Jaegul
dc.contributor.authorLee, Daniel
dc.contributor.authorDilkina, Bistra
dc.contributor.authorZha, Hongyuan
dc.contributor.authorPark, Haesun
dc.date.accessioned2013-10-23T20:53:32Z
dc.date.available2013-10-23T20:53:32Z
dc.date.issued2013
dc.identifier.urihttp://hdl.handle.net/1853/49249
dc.descriptionResearch areas: Web mining, Machine learning, Data mining.en_US
dc.description.abstractMicro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding micro-financial transactions available at Kiva. Based on this approach, we achieved a competitive performance of 0.84 AUC value in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan’s geolocation, a borrower’s gender, a field partner’s reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders’ background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCSE Technical Reports ; GT-CSE-13-05en_US
dc.subjectHeterogeneous dataen_US
dc.subjectMaximum entropy distributionen_US
dc.subjectMicrofinanceen_US
dc.subjectSocial groupen_US
dc.titleTo Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendationen_US
dc.typeTechnical Reporten_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Computational Science and Engineeringen_US
dc.embargo.termsnullen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record