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Understanding and Promoting Micro-Finance Activities in Kiva.org

Understanding and Promoting Micro-Finance Activities in Kiva.org. Jaegul Choo *, Changhyun Lee*, Daniel Lee † , Hongyuan Zha *, and Haesun Park* *Georgia Institute of Technology † Georgia Tech Research Institute jaegul.choo@cc.gatech.edu http://www.cc.gatech.edu/~joyfull/

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Understanding and Promoting Micro-Finance Activities in Kiva.org

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  1. Understanding and Promoting Micro-Finance Activities in Kiva.org JaegulChoo*, Changhyun Lee*, Daniel Lee†, HongyuanZha*, and HaesunPark* *Georgia Institute of Technology †Georgia Tech Research Institute jaegul.choo@cc.gatech.eduhttp://www.cc.gatech.edu/~joyfull/ 2014 ACM International Conference on Web Search and Data Mining (WSDM) New York City, NY, USA 02/27/2014

  2. Micro-Financingin Kiva.org 2

  3. How Micro-Financing Works 3

  4. Kiva Datahttp://tinyurl.com/kiva-matlab-data Entities • Lender (1M): sign-up date, loan_because, occupation, location, … • Loan(560K): description, amount, location, sector, … • Lending team (25K): type, #members, #funded loans, … • Field partner (250): due-diligence type, delinquency rate, location, … • Borrower (1M): name, gender Graphs • Lender-loan (12M edges): who funds which loan • Lender-team (300k edges): who is a member of which team 4

  5. Modeling Micro-Financing Activities Target task • Modeling likelihood of funding, π(f(u, l)), given a feature vector f for a lender (user) u and a loan l. Supervised learning • Label: 1 if a lender u funded a loan l, and 0 otherwise • Learner: gradient-boosting tree 5

  6. Feature Generation Graph-based feature integration u l 6

  7. Cold-Start Problem What if lenders and loans have no links, e.g., brand-new lender and loan? u l 7

  8. Feature Alignment via Joint Nonnegative Matrix Factorization How it works Step 1: Learning mapping Step 2: Map data to an aligned space Lender space Loan space Aligned space 8

  9. ROC Curve Compared between different lender groups w.r.t. the number of previous loans, m. AUC: 0.79 AUC: 0.92 9 Passive lender (m = 5) Active lender (m = 25)

  10. Variable Importance Analysis • Time between two consecutive loans is important. 10 AUC improvement over .5 when using only a particular feature group AUC degradation due to the exclusion of a particular feature group

  11. Temporal Lending Behavior People tend to keep funding loans continuously, but lose interest over time. 11

  12. Temporal Lending Behavior People tend to keep funding loans continuously, but lose interest over time. Loans are generally paid in half a year or a full year. 12

  13. Temporal Lending Behavior People tend to keep funding loans continuously, but lose interest over time. Loans are generally paid in half a year or a full year. Passive lenders often recycle money instead of spending more money. 13

  14. Variable Importance Analysis • Time between two consecutive loans is important. • Loan delinquencies discourage passive lenders although they do not impact active lenders as much. 14 AUC improvement over .5 when using only a particular feature group AUC degradation due to the exclusion of a particular feature group

  15. Variable Importance Analysis • Time between two consecutive loans is important. • Loan delinquencies discourage passive lenders although they do not impact active lenders as much. • Lending teams greatly influence active lenders. 15 AUC improvement over .5 when using only a particular feature group AUC degradation due to the exclusion of a particular feature group

  16. Performance Improvement due to Feature Alignment Aligned features • Lenders’ occupational_info vs. loans’ description • Lenders’ loan_because vs. loans’ description Baseline • All the different textual fields are represented in a single space (using a common vocabulary set), and NMF is applied. 16

  17. Aligned Topics • People working at a school like to fund family-related loans. 17

  18. Aligned Topics • People working at a school like to fund family-related loans. • Students like to fund business-related loans. 18

  19. Comments on Other Papers Inferring the Impacts of Social Media on Crowdfunding • Associating social media with micro-financing activities, e.g., dynamics of team activities Is a Picture Really Worth a Thousand Words? - On the Role of Images in E-commerce • Analyzing the effects of pictures in loan pages, e.g., borrowers’ picture 19

  20. Summary We modeled micro-financing activities at Kiva.org as a binary classification/regression problem. • Graph-based feature integration • Feature alignment via joint NMF We provided in-depth analysis and obtained knowledge about users’ lending behaviors. THANK YOU(Data set: http://tinyurl.com/kiva-matlab-data) JaegulChoojaegul.choo@cc.gatech.eduhttp://www.cc.gatech.edu/~joyfull/ 20

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