研究著作內容
Fairness-Aware Loan Recommendation for Microfinance Services
(NOTE: Sheng-Wei Chen is also known as Kuan-Ta Chen.)

Abstract
Up to date, more than 15 billion US dollars have been invested in microfinance that benefited more than 160 million people in developing countries. The Kiva organization is one of the successful examples that use a decentralized matching process to match lenders and borrowers. Interested lenders from around the world can look for cases among thousands of applicants they found promising to lend the money to. But how can loan borrowers and lenders be successfully matched up in a microfinance platform like Kiva? We argue that a sophisticate recommender not only pairs up loan lenders and borrowers in accordance to their preferences, but should also help to diversify the distribution of donations to reduce the inequality of loans is highly demanded, as altruism, like any resource, can be congestible.

In this paper, we propose a fairness-aware recommendation system based on one-class collaborative-filtering techniques for charity and micro-loan platform such as Kiva.org. Our experiments on real dataset indicates that the proposed method can largely improve the loan distribution fairness while retaining the accuracy of recommendations.


Materials
Citation
Eric L. Lee, Jing-Kai Lou, Wei-Ming Chen, Yen-Chi Chen, Shou-De Lin, Yen-Sheng Chiang, and Kuan-Ta Chen, "Fairness-Aware Loan Recommendation for Microfinance Services," In Proceedings of SocialCom 2014, 2014.

BibTex
@INPROCEEDINGS{lee14:microfinance,
  TITLE      = {Fairness-Aware Loan Recommendation for Microfinance Services},
  AUTHOR     = {Eric L. Lee and Jing-Kai Lou and Wei-Ming Chen and Yen-Chi Chen and Shou-De Lin and Yen-Sheng Chiang and Kuan-Ta Chen},
  YEAR       = {2014},
  BOOKTITLE  = {Proceedings of SocialCom 2014}
}