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.

Original languageEnglish (US)
Title of host publicationWSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery, Inc
Pages477-485
Number of pages9
Volume2018-Febuary
ISBN (Electronic)9781450355810
DOIs
StatePublished - Feb 2 2018
Event11th ACM International Conference on Web Search and Data Mining, WSDM 2018 - Marina Del Rey, United States
Duration: Feb 5 2018Feb 9 2018

Other

Other11th ACM International Conference on Web Search and Data Mining, WSDM 2018
CountryUnited States
CityMarina Del Rey
Period2/5/182/9/18

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Keywords

  • Crowdfunding
  • Information seeking
  • Q&A
  • Socioeconmics

ASJC Scopus subject areas

  • Computer Science Applications
  • Software
  • Computer Networks and Communications
  • Information Systems

Cite this

Ranganath, S., Beigi, G., & Liu, H. (2018). Leveraging implicit contribution amounts to facilitate microfinancing requests. In WSDM 2018 - Proceedings of the 11th ACM International Conference on Web Search and Data Mining (Vol. 2018-Febuary, pp. 477-485). Association for Computing Machinery, Inc. https://doi.org/10.1145/3159652.3159679