Suhas Ranganath
Huan Liu
ABSTRACT
The emergence of online microfinancing platforms provides new opportunities for people to seek financial assistance from a large number of potential contributors. However, these platforms deal with a huge number of requests, making it hard for the requesters to get assistance for their financial needs. Designing algorithms to identify potential contributors for a given request will assist in satisfying financial needs of requesters and improve the effectiveness of microfinancing platforms. Existing work correlates requests with contributor interests and profiles to design feature based approaches for recommending projects to prospective contributors. However, contributing money to financial requests has a cost on contributors which can affect his inclination to contribute in the future . Literature in economic behavior has investigated the manner in which memory of past contribution amounts affects user inclination to contribute to a given request. To systematically investigate whether these characteristics of economic behavior would help to facilitate requests in online microfinancing platforms, we present a novel framework to identify contributors for a given request from their past financial information. Individual contribution amounts are not publicly available, so we draw from financial modeling literature to model the implicit contribution amounts made to past requests. We evaluate the framework on two microfinancing platforms to demonstrate its effectiveness in identifying contributors.
- Wei Ai, Roy Chen, Yan Chen, Qiaozhu Mei, and Webb Phillips . 2016. Recommending teams promotes prosocial lending in online microfinance. Proceedings of the National Academy of Sciences, Vol. 113, 52 (2016), 14944--14948.Google Scholar
Cross Ref
- Tim Althoff and Jure Leskovec . 2015. Donor retention in online crowdfunding communities: A case study of donorschoose. org WWW. ACM. Google ScholarDigital Library
- Jisun An, Daniele Quercia, and Jon Crowcroft . 2014. Recommending investors for crowdfunding projects. WWW. ACM. Google ScholarDigital Library
- Paul N Bloom . 2009. Overcoming consumption constraints through social entrepreneurship. Journal of Public Policy & Marketing Vol. 28, 1 (2009), 128--134.Google ScholarCross Ref
- Tillman Bruett et almbox. . 2007. Cows, Kiva, and Prosper. Com: how disintermediation and the internet are changing microfinance. Community Development Investment Review 2 (2007), 44--50.Google Scholar
- Roy Chen, Yan Chen, Yang Liu, and Qiaozhu Mei . 2015. Does team competition increase pro-social lending? Evidence from online microfinance. Games and Economic Behavior (2015).Google Scholar
- Jaegul Choo, Changhyun Lee, Daniel Lee, Hongyuan Zha, and Haesun Park . 2014 b. Understanding and promoting micro-finance activities in kiva. org WSDM. ACM. Google ScholarDigital Library
- Jaegul Choo, Daniel Lee, Bistra Dilkina, Hongyuan Zha, and Haesun Park . 2014 a. To gather together for a better world: understanding and leveraging communities in micro-lending recommendation. In WWW. ACM. Google ScholarDigital Library
- Shijie Deng . 2000. Stochastic models of energy commodity prices and their applications: Mean-reversion with jumps and spikes. Berkeley: University of California Energy Institute.Google Scholar
- Merel Van Diepen, Bas Donkers, and Philip Hans Franses . 2009. Dynamic and competitive effects of direct mailings: A charitable giving application. Journal of Marketing Research Vol. 46, 1 (2009), 120--133.Google ScholarCross Ref
- Nicole B Ellison, Rebecca Gray, Jessica Vitak, Cliff Lampe, and Andrew T Fiore . 2013. Calling All Facebook Friends: Exploring Requests for Help on Facebook. International AAAI Conference on Web and Social Media.Google Scholar
- Matt Flannery . 2007. Kiva and the birth of person-to-person microfinance. Innovations, Vol. 2, 1--2 (2007), 31--56.Google Scholar
- Jeff Galak, Deborah Small, and Andrew T Stephen . 2011. Microfinance decision making: A field study of prosocial lending. Journal of Marketing Research Vol. 48, SPL (2011), S130--S137.Google ScholarCross Ref
- Brent Hecht, Jaime Teevan, Meredith Morris, and Dan Liebling . 2012. SearchBuddies: Bringing Search Engines into the Conversation ICWSM.Google Scholar
- Julie S Hui, Michael D Greenberg, and Elizabeth M Gerber . 2014. Understanding the role of community in crowdfunding work CSCW. ACM. Google ScholarDigital Library
- Jin-Woo Jeong, Meredith Ringel Morris, Jaime Teevan, and Dan Liebling . 2013. A Crowd-Powered Socially Embedded Search Engine. ICWSM.Google Scholar
- Fang Jin, Rupinder Paul Khandpur, Nathan Self, Edward Dougherty, Sheng Guo, Feng Chen, B Aditya Prakash, and Naren Ramakrishnan . 2014. Modeling mass protest adoption in social network communities using geometric brownian motion KDD. ACM. Google ScholarDigital Library
- Ioannis Karatzas and Steven Shreve . 2012. Brownian motion and stochastic calculus. Vol. Vol. 113. Springer Science & Business Media.Google Scholar
- Daniel D Lee and H Sebastian Seung . 2001. Algorithms for non-negative matrix factorization. NIPS.Google Scholar
- Uichin Lee, Hyanghong Kang, Eunhee Yi, Mun Yi, and Jussi Kantola . 2012. Understanding mobile q&a usage: An exploratory study CHI. ACM. Google ScholarDigital Library
- Jalal Mahmud, Michelle Zhou, Nimrod Megiddo, Jeffrey Nichols, and Clemens Drews . 2014. Optimizing the selection of strangers to answer questions in social media. arXiv preprint arXiv:1404.2013 (2014).Google Scholar
- Meredith Ringel Morris, Jaime Teevan, and Katrina Panovich . 2010. What do people ask their social networks, and why?: a survey study of status message q&a behavior CHI. ACM. Google ScholarDigital Library
- Bernt Oksendal . 2003. Stochastic differential equations. Stochastic differential equations. Springer, 65--84.Google Scholar
- Sharoda A Paul, Lichan Hong, and Ed H Chi . 2011. Is Twitter a Good Place for Asking Questions? A Characterization Study ICWSM.Google Scholar
- Craig Pirrong . 2011. Commodity price dynamics: A structural approach. Cambridge University Press.Google Scholar
- Ioana Popescu and Yaozhong Wu . 2007. Dynamic pricing strategies with reference effects. Operations Research, Vol. 55, 3 (2007), 413--429. Google ScholarDigital Library
- Vineeth Rakesh, Wang-Chien Lee, and Chandan K Reddy . 2016. Probabilistic Group Recommendation Model for Crowdfunding Domains WSDM. ACM. Google ScholarDigital Library
- Suhas Ranganath, Fred Morstatter, Xia Hu, Jiliang Tang, Suhang Wang, and Huan Liu . 2016. Predicting Online Protest Participation of Social Media Users AAAI. Google ScholarDigital Library
- Suhas Ranganath, Jiliang Tang, Xia Hu, Hari Sundaram, and Huan Liu . 2015. Leveraging Social Foci for Information Seeking in Social Media AAAI. Google ScholarDigital Library
- Jacob Solomon, Wenjuan Ma, and Rick Wash . 2016. Highly Successful Projects Inhibit Coordination on Crowdfunding Sites CHI. ACM. Google ScholarDigital Library
- Peter Tankov . 2003. Financial modelling with jump processes. Vol. Vol. 2. CRC press.Google Scholar
- Jiang Yang, Meredith Ringel Morris, Jaime Teevan, Lada A Adamic, and Mark S Ackerman . 2011. Culture Matters: A Survey Study of Social Q&A Behavior. ICWSM (2011).Google Scholar
- Yongfeng Zhang, Qi Zhao, Yi Zhang, Daniel Friedman, Min Zhang, Yiqun Liu, and Shaoping Ma . 2016. Economic recommendation with surplus maximization. WWW. Google ScholarDigital Library
- Haijun Zhou . 2003 a. Distance, dissimilarity index, and network community structure. Physical review, Vol. 67, 6 (2003), 061901.Google Scholar
- Haijun Zhou . 2003 b. Network landscape from a Brownian particle's perspective. Physical Review E, Vol. 67, 4 (2003), 041908.Google ScholarCross Ref
- Haijun Zhou and Reinhard Lipowsky . 2004. Network brownian motion: A new method to measure vertex-vertex proximity and to identify communities and subcommunities. Computational Science-ICCS 2004. Springer, 1062--1069.Google Scholar
Index Terms
- Leveraging Implicit Contribution Amounts to Facilitate Microfinancing Requests
Recommendations
Collateral Valuation and Borrower Financial Constraints: Evidence from the Residential Real Estate Market
Financially constrained borrowers have the incentive to influence the appraisal process in order to increase borrowing or reduce the interest rate. We document that the average valuation bias for residential refinance transactions is above 5%. The bias ...
Web Portal for Matching Loan Requests and Investment Offers in Peer-To-Peer Lending
Online Peer-To-Peer lending has seen some growing media attention since its recent creation. Nonetheless, the systems which provide deal brokerage in this context have yet to be given significant consideration within the scientific community. This paper ...
The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending
This study goes beyond peer-to-peer (P2P) lending credit scoring systems by proposing a profit scoring. Credit scoring systems estimate loan default probability. Although failed borrowers do not reimburse the entire loan, certain amounts may be ...
Comments