Title:
To Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendation
To Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendation
| dc.contributor.author | Choo, Jaegul | |
| dc.contributor.author | Lee, Daniel | |
| dc.contributor.author | Dilkina, Bistra | |
| dc.contributor.author | Zha, Hongyuan | |
| dc.contributor.author | Park, Haesun | |
| dc.contributor.corporatename | Georgia Institute of Technology. College of Computing | en_US |
| dc.contributor.corporatename | Georgia Institute of Technology. School of Computational Science and Engineering | en_US |
| dc.date.accessioned | 2013-10-23T20:53:32Z | |
| dc.date.available | 2013-10-23T20:53:32Z | |
| dc.date.issued | 2013 | |
| dc.description | Research areas: Web mining, Machine learning, Data mining. | en_US |
| dc.description.abstract | Micro-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.embargo.terms | null | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/49249 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | CSE Technical Reports ; GT-CSE-13-05 | en_US |
| dc.subject | Heterogeneous data | en_US |
| dc.subject | Maximum entropy distribution | en_US |
| dc.subject | Microfinance | en_US |
| dc.subject | Social group | en_US |
| dc.title | To Gather Together for a Better World: Understanding and Leveraging Communities in Micro-lending Recommendation | en_US |
| dc.type | Text | |
| dc.type.genre | Technical Report | |
| dspace.entity.type | Publication | |
| local.contributor.author | Park, Haesun | |
| local.contributor.corporatename | College of Computing | |
| local.contributor.corporatename | School of Computational Science and Engineering | |
| local.relation.ispartofseries | College of Computing Technical Report Series | |
| local.relation.ispartofseries | School of Computational Science and Engineering Technical Report Series | |
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