https://scholars.lib.ntu.edu.tw/handle/123456789/489806
標題: | Fairness-Aware Loan Recommendation for Microfinance Services. | 作者: | Lee, Eric L. Lou, Jing-Kai Chen, Wei-Ming Chen, Yen-Chi Lin, Shou-De Chiang, Yen-Sheng SHOU-DE LIN |
公開日期: | 2014 | 起(迄)頁: | 3:1-3:4 | 來源出版物: | Proceedings of the 2014 International Conference on Social Computing, Beijing, China, August 04 - 07, 2014 | 摘要: | 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. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/489806 | DOI: | 10.1145/2639968.2640064 | SDG/關鍵字: | Collaborative filtering; Developing countries; Social networking (online); Collaborative filtering techniques; Matching process; Microfinance; US dollar; Finance |
顯示於: | 資訊工程學系 |
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