Non-profit Micro-finance organizations provide loaning opportunities to eradicate poverty by financially equipping impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to those who have little. Kiva.org, a widely-used crowd-funded micro-financial service, provides researchers with an extensive amount of publicly available data containing a rich set of heterogeneous information regarding micro-financial transactions. Our objective in this paper is to identify the key factors that encourage people to make micro-financing donations, and ultimately, to keep them actively involved. In our contribution to further promote a healthy micro-finance ecosystem, we detail our personalized loan recommendation system which we formulate as a supervised learning problem where we try to predict how likely a given lender will fund a new loan. We construct the features for each data item by utilizing the available connectivity relationships in order to integrate all the available Kiva data sources. For those lenders with no such relationships, e.g., first-time lenders, we propose a novel method of feature construction by computing joint nonnegative matrix factorizations. Utilizing gradient boosting tree methods, a state-of-the-art prediction model, we are able to achieve up to 0.92 AUC (area under the curve) value, which shows the potential of our methods for practical deployment. Finally, we point out several interesting phenomena on lenders' social behaviors in micro-finance activities.