Jaegul Choo *, Daniel Lee † , Bistra Dilkina * , Hongyuan Zha * , and Haesun Park *

1. To Gather Together for a Better World: Leveraging Communities in Micro-Lending Recommendation JaegulChoo*, Daniel Lee†, BistraDilkina*, HongyuanZha*, and HaesunPark* *Georgia Institute of Technology †Georgia Tech Research Institute 2014 International World Wide Web Conference (WWW)

2. Micro-Financingin Kiva.org 2

3. How Micro-Financing Works 3

4. Impact of Lending Teams 4

5. Overview Main idea Understanding and Leveraging lending teams in modeling lending activities Outline Modeling lending activities Lending activity prediction Analysis on team behaviors Team affiliation prediction 5

6. 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 6

7. Modeling Lending Activities Target task Given a lender (user) u and a loan l, and his/her team affiliation t, Modeling likelihood/density of funding, πt(f(u, l)), where f(u, l)is a feature vector for (u, l). 7

8. Feature GenerationGraph-based Feature Integration u l 8

9. Modeling Lending Activities Target task Given a lender (user) u and a loan l, and his/her team affiliation t, Modeling likelihood/density of funding, πt(f(u, l)), where f(u, l)is a feature vector for (u, l). Supervised learning Label: 1 if a lender u funded a loan l, and 0 otherwise Model: Maximum-entropy distribution model 9

10. Why Maximum-Entropy Distribution (Maxent) Model? Negative samples are NOT really negative. No funding does NOT mean that s/he dislikes the loan. Maxent model Considers labeled data as presence-only data (rather than presence-vs-absence data) Let’s give as much probability as possible to negative samples Density: Optimization: 10

11. Ensemble Model Based on Team-Specific Models • L1-regularized logistic regression Motivation What if team information is NOT known for a lender u ? Weighted Stacking Approach 11

12. Overview Main idea Leveraging lending teams in modeling lending activities Outline Modeling lending activities Lending activity prediction Analysis on team behaviors Team affiliation prediction 12

13. Experimental Setup1. Team and Data Selection We selected top 200 lending teams (70% of the total lending amount) For each team, we selected 5,000 lender-loan pairs where funding happened (positive) and 5,000 other pairs (negative). 200 team-specific models + no-team model (201 in total) 13

14. Experimental Setup2. Used Features 938 dimensions in total Textual (600 dimensions): a lender’ loan because and a loan’s loan description Loan sector (45 dimensions): industry of a loan, e.g., agriculture, food, retail, etc. Geo-location (228 dimensions): a lender’s and loan’s location Loan delinquency/default (13 dimensions): delinquency/default rate of a lender’s previous loans Field partner (33 dimensions): loan amount, rating Borrower (12 dimensions): a borrower’s gender and has_picture Temporal information (7 dimensions): relative time difference between two consecutive loans of a lender 14

15. Experimental Setup3. Compared Methods Aggregate-data model: a single model using aggregated data (Team information is encoded as a 60-dimensional feature.) Team-specific model Ensemble model 15

16. Prediction ResultAUC values over 200 Teams Ensemble model works best. Aggregate-data Ensemble Team-specific Loan feature Loan feature + Lender feature Loan feature + Lender feature + Correlation feature 16

17. What Do Teams Care?Variable Importance Analysis Commonalities Time interval between two consecutive loans is important. Loan delinquencies highly discourage further lending. Differences Teams carefully choose loans depending on various aspects. 17

18. Which Teams Care Which Aspect?Top Five Teams for Feature Groups Loan sector (Industry) ‘KivaFriends - Agriculture Loans’, ‘Ravelry.com’, ‘101 Cookbooks’, ‘Give Green - Environmental Loans’, ‘Thailand’ Geo-location ‘Para Mexico’, ‘Philippines’, ‘Kiva Muslims’, ‘Kiva Detroit’, ‘Portugal’ Field partner ‘Amici di Raffaele (Raphael’s Friends)’, ‘Woodlands’, ‘Compadres’, ‘Lauren Avezzie’, ‘Kiva Jews’ Borrower ‘women empowering women’, ‘HALF THE SKY: Empowering Women’, ‘Georgia Southern Alumni’, ‘www.idu.cc’, ‘TaretoMaa’ 18

19. Which Teams have Outlying Behaviors? Two outlying teams • ‘Expired Loans’ • ‘Late Loaning Lenders’ Time taken for a loan to be fully funded Visualization of variable importance vectors (generated by principal component analysis) 19

20. Overview Main idea Leveraging lending teams in modeling lending activities Outline Modeling lending activities Lending activity prediction Analysis on team behaviors Team affiliation prediction 20

21. Team Affiliation Prediction Given a lender u and a team t, the likelihood L(u, t)is where liuare the first c loans of u. Supervised Learning Features f(u, t): Similarity-weighted likelihood values Label: 1 if a lender uis affiliated with the team t Learner: Logistic regression 21

22. Team Affiliation PredictionResults 22

23. Conclusion and Future Work In summary, we analyzed lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction Future Work Social influence within a team 23

24. Conclusion and Future Work Co-lending graph between lenders Kiva Christians Atheists, Agnostics, … In summary, we presented lending teams in a micro-finance service, Kiva.org.Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction Future Work Social influence within a team 24

25. Conclusion and Future Work In summary, we presented lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction Future Work Social influence within a team Compare and contrast between teams 25

26. Conclusion and Future Work Common topics ‘Thai’ `Greece’ In summary, we presented characteristics of lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse behaviors of lending teams Performed team affiliation prediction Future Work Social influence within a team Compare and contrast between teams 26

27. Conclusion and Future Work Currently on the Job Market Thank you! JaegulChoojaegul.choo@cc.gatech.edu http://www.cc.gatech.edu/~joyfull/ In summary, we presented lending teams in a micro-finance service, Kiva.org. Modeled team activities as a Maxent model Discovered diverse team characteristics Performed team affiliation prediction On-going/future Work Social influence within a team Compare and contrast between teams Evolution of lending teams 27