To Gather Together for a Better World: Leveraging Communities in Micro-Lending Recommendation Jaegul...
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- Slide 1
- To Gather Together for a Better World: Leveraging Communities
in Micro-Lending Recommendation Jaegul Choo*, Daniel Lee , Bistra
Dilkina*, Hongyuan Zha*, and Haesun Park* *Georgia Institute of
Technology Georgia Tech Research Institute 2014 International World
Wide Web Conference (WWW)
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- Micro- Financing in Kiva.org 2
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- How Micro-Financing Works 3
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- Impact of Lending Teams 4
- Slide 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
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- Kiva Data http://tinyurl.com/kiva-matlab-data
http://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
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- 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, t (f(u, l)), where f(u, l) is a
feature vector for (u, l). 7
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- Feature Generation Graph-based Feature Integration 8 u l
- Slide 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, t (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
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- 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) Lets give
as much probability as possible to negative samples Density:
Optimization: 10
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- Ensemble Model Based on Team-Specific Models Motivation What if
team information is NOT known for a lender u ? Weighted Stacking
Approach 11 L1-regularized logistic regression
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- 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
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- Experimental Setup 1. 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
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- Experimental Setup 2. Used Features 938 dimensions in total
Textual (600 dimensions): a lender loan because and a loans loan
description Loan sector (45 dimensions): industry of a loan, e.g.,
agriculture, food, retail, etc. Geo-location (228 dimensions): a
lenders and loans location Loan delinquency/default (13
dimensions): delinquency/default rate of a lenders previous loans
Field partner (33 dimensions): loan amount, rating Borrower (12
dimensions): a borrowers gender and has_picture Temporal
information (7 dimensions): relative time difference between two
consecutive loans of a lender 14
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- Experimental Setup 3. 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
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- Prediction Result AUC values over 200 Teams 16 Loan feature
Loan feature + Lender feature Loan feature + Lender feature +
Correlation feature Aggregate- data Team- specific Ensemble
Ensemble model works best.
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- 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
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- 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 (Raphaels
Friends), Woodlands, Compadres, Lauren Avezzie, Kiva Jews Borrower
women empowering women, HALF THE SKY: Empowering Women, Georgia
Southern Alumni, www.idu.cc, Tareto Maa 18
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- Which Teams have Outlying Behaviors? Visualization of variable
importance vectors (generated by principal component analysis) 19
Two outlying teams Expired Loans Late Loaning Lenders Time taken
for a loan to be fully funded
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- 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
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- Team Affiliation Prediction Given a lender u and a team t, the
likelihood L(u, t) is where l i u are the first c loans of u.
Supervised Learning Features f(u, t): Similarity-weighted
likelihood values Label: 1 if a lender u is affiliated with the
team t Learner: Logistic regression 21
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- Team Affiliation Prediction Results 22 Mean reciprocal rank
(MRR) Similarity-weighted model.1482 (.0402) Direct team-specific
model.0851 (.0365) Aggregate-data model.0548 (.0210) Random
assignment.0294
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- 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
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- Conclusion and Future Work 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 Kiva Christians Atheists,
Agnostics, Co-lending graph between lenders
- Slide 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
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- Conclusion and Future Work 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 Thai`Greece Common topics
- Slide 27
- 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 On-going/future Work Social influence
within a team Compare and contrast between teams Evolution of
lending teams 27 Thank you! Jaegul Choo jaegul.choo@cc.gatech.edu
jaegul.choo@cc.gatech.edu http://www.cc.gatech.edu/~joyfull/
Currently on the Job Market