Title:
A Better World for All: Understanding and Promoting Micro-finance Activities in Kiva.org
A Better World for All: Understanding and Promoting Micro-finance Activities in Kiva.org
| dc.contributor.author | Choo, Jaegul | |
| dc.contributor.author | Lee, Changhyun | |
| dc.contributor.author | Lee, Daniel | |
| 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 | 2013-10-07T18:31:24Z | |
| dc.date.available | 2013-10-07T18:31:24Z | |
| dc.date.issued | 2013 | |
| dc.description | Research areas: Web mining, Machine learning, and Data mining | en_US |
| dc.description.abstract | Micro-finance organizations provide non-profit loaning opportunities to eradicate poverty by financially equipping impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to those who have little. Kiva.org, a widely-used crowd-funded micro-financial service, provides researchers with an extensive amount of openly downloadable data containing a wealthy set of heterogeneous information regarding micro-financial transactions. Our objective is to identify the key factors that encourage people to make micro-financing donations, and ultimately, to keep them actively involved. In our contribution to further promote a healthy micro-finance ecosystem, we detail our personalized loan recommendation system which we formulate as a supervised learning problem where we try to predict how likely a given lender will fund a new loan. We construct the features for each data item by utilizing the available connectivity relationships in order to integrate all the available Kiva data types. For those lenders with no such relationships, e.g., first-time lenders, we propose a novel method of feature construction by computing joint nonnegative matrix factorization. By using a gradient boosting tree, a state-of-the-art prediction model, we are able to achieve up to 0.92 AUC (area under the curve) value, which shows that our work is ready for use in practice. Finally, we reveal various interesting knowledge about lenders’ social behaviors in micro-finance activities. | en_US |
| dc.embargo.terms | null | en_US |
| dc.identifier.uri | http://hdl.handle.net/1853/49182 | |
| dc.language.iso | en_US | en_US |
| dc.publisher | Georgia Institute of Technology | en_US |
| dc.relation.ispartofseries | CSE Technical Reports ; GT-CSE-13-03 | en_US |
| dc.subject | Gradient boosting tree | en_US |
| dc.subject | Heterogeneous data | en_US |
| dc.subject | Joint matrix factorization | en_US |
| dc.subject | Maximum entropy distribution | en_US |
| dc.subject | Microfinance | en_US |
| dc.subject | Social group | en_US |
| dc.title | A Better World for All: Understanding and Promoting Micro-finance Activities in Kiva.org | 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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