research-article

To gather together for a better world: understanding and leveraging communities in micro-lending recommendation

Authors:

Bistra Dilkina,

Haesun ParkAuthors Info & Claims

WWW '14: Proceedings of the 23rd international conference on World wide web

Pages 249 - 260

https://doi.org/10.1145/2566486.2568020

Published: 07 April 2014 Publication History

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 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 geo-location, 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.

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Cited By

MORO-VISCONTI RRAMBAUD SPASCUAL J(2022)DO FIELD PARTNERS ADD VALUE TO CROWDFUNDED MICROFINANCE? AN INDUSTRY APPROACHJournal of Financial Management, Markets and Institutions10.1142/S2282717X2250007410:02Online publication date: 16-Nov-2022
https://doi.org/10.1142/S2282717X22500074
Zhang LWu XZhao HCheng FLiu Q(2022)Personalized Recommendation in P2P Lending Based on Risk-Return Management: A Multi-Objective PerspectiveIEEE Transactions on Big Data10.1109/TBDATA.2020.29934468:4(1141-1154)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TBDATA.2020.2993446
Xiao YLiu CZheng WWang HHsu C(2021)A feature interaction learning approach for crowdfunding project recommendationApplied Soft Computing10.1016/j.asoc.2021.107777112(107777)Online publication date: Nov-2021
https://doi.org/10.1016/j.asoc.2021.107777
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Index Terms

To gather together for a better world: understanding and leveraging communities in micro-lending recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Other conferences

WWW '14: Proceedings of the 23rd international conference on World wide web

April 2014

926 pages

ISBN:9781450327442

DOI:10.1145/2566486

General Chair:
Chin-Wan Chung
Korea Advanced Institute of Science and Technology, Korea
,
Program Chairs:
Andrei Broder
Google Inc., USA
,
Kyuseok Shim
Seoul National University, Korea
,
Torsten Suel
New York University, USA

Copyright © 2014 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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IW3C2: International World Wide Web Conference Committee

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 April 2014

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WWW '14

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IW3C2

WWW '14: 23rd International World Wide Web Conference

April 7 - 11, 2014

Seoul, Korea

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WWW '14 Paper Acceptance Rate 84 of 645 submissions, 13%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

MORO-VISCONTI RRAMBAUD SPASCUAL J(2022)DO FIELD PARTNERS ADD VALUE TO CROWDFUNDED MICROFINANCE? AN INDUSTRY APPROACHJournal of Financial Management, Markets and Institutions10.1142/S2282717X2250007410:02Online publication date: 16-Nov-2022
https://doi.org/10.1142/S2282717X22500074
Zhang LWu XZhao HCheng FLiu Q(2022)Personalized Recommendation in P2P Lending Based on Risk-Return Management: A Multi-Objective PerspectiveIEEE Transactions on Big Data10.1109/TBDATA.2020.29934468:4(1141-1154)Online publication date: 1-Aug-2022
https://doi.org/10.1109/TBDATA.2020.2993446
Xiao YLiu CZheng WWang HHsu C(2021)A feature interaction learning approach for crowdfunding project recommendationApplied Soft Computing10.1016/j.asoc.2021.107777112(107777)Online publication date: Nov-2021
https://doi.org/10.1016/j.asoc.2021.107777
Zhao HJin BLiu QGe YChen EZhang XXu T(2020)Voice of Charity: Prospecting the Donation Recurrence & Donor Retention in CrowdfundingIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2019.290619932:8(1652-1665)Online publication date: 1-Aug-2020
https://doi.org/10.1109/TKDE.2019.2906199
Liu WGuo JSonboli NBurke RZhang SBogers TSaid ABrusilovsky PTikk D(2019)Personalized fairness-aware re-ranking for microlendingProceedings of the 13th ACM Conference on Recommender Systems10.1145/3298689.3347016(467-471)Online publication date: 10-Sep-2019
https://dl.acm.org/doi/10.1145/3298689.3347016
Ranganath SBeigi GLiu HChang YZhai CLiu YMaarek Y(2018)Leveraging Implicit Contribution Amounts to Facilitate Microfinancing RequestsProceedings of the Eleventh ACM International Conference on Web Search and Data Mining10.1145/3159652.3159679(477-485)Online publication date: 2-Feb-2018
https://dl.acm.org/doi/10.1145/3159652.3159679
Ren XXu LZhao TZhu CGuo JChen E(2018)Tracking and Forecasting Dynamics in Crowdfunding: A Basis-Synthesis Approach2018 IEEE International Conference on Data Mining (ICDM)10.1109/ICDM.2018.00161(1212-1217)Online publication date: Nov-2018
https://doi.org/10.1109/ICDM.2018.00161
Zhao HGe YLiu QWang GChen EZhang H(2017)P2P Lending SurveyACM Transactions on Intelligent Systems and Technology10.1145/30788488:6(1-28)Online publication date: 24-Jul-2017
https://dl.acm.org/doi/10.1145/3078848
Zhao HLiu QWang GGe YChen EKrishnapuram BShah MSmola AAggarwal CShen DRastogi R(2016)Portfolio Selections in P2P LendingProceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining10.1145/2939672.2939861(2075-2084)Online publication date: 13-Aug-2016
https://dl.acm.org/doi/10.1145/2939672.2939861
Li YRakesh VReddy CBennett PJosifovski VNeville JRadlinski F(2016)Project Success Prediction in Crowdfunding EnvironmentsProceedings of the Ninth ACM International Conference on Web Search and Data Mining10.1145/2835776.2835791(247-256)Online publication date: 8-Feb-2016
https://dl.acm.org/doi/10.1145/2835776.2835791
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