Original Contribution: Stacked generalization
Neural Networks
A maximum entropy approach to natural language processing
Computational Linguistics
Inducing Features of Random Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
Opinion-Based Filtering through Trust
CIA '02 Proceedings of the 6th International Workshop on Cooperative Information Agents VI
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
A maximum entropy approach to species distribution modeling
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Investigating interactions of trust and interest similarity
Decision Support Systems
Beyond Microblogging: Conversation and Collaboration via Twitter
HICSS '09 Proceedings of the 42nd Hawaii International Conference on System Sciences
Architecture and algorithms for a distributed reputation system
iTrust'03 Proceedings of the 1st international conference on Trust management
"I loan because...": understanding motivations for pro-social lending
Proceedings of the fifth ACM international conference on Web search and data mining
UTOPIAN: User-Driven Topic Modeling Based on Interactive Nonnegative Matrix Factorization
IEEE Transactions on Visualization and Computer Graphics
Understanding and promoting micro-finance activities in Kiva.org
Proceedings of the 7th ACM international conference on Web search and data mining
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Micro-finance organizations provide non-profit lending opportunities to mitigate poverty by financially supporting impoverished, yet skilled entrepreneurs who are in desperate need of an institution that lends to them. In Kiva.org, a widely-used crowd-funded micro-financial service, a vast amount of micro-financial activities are done by lending teams, and thus, understanding their diverse characteristics is crucial in maintaining a healthy micro-finance ecosystem. As the first step for this goal, we model different lending teams by using a maximum-entropy distribution approach based on a wealthy set of heterogeneous information regarding micro-financial transactions available at Kiva. Based on this approach, we achieved a competitive performance in predicting the lending activities for the top 200 teams. Furthermore, we provide deep insight about the characteristics of lending teams by analyzing the resulting team-specific lending models. We found that lending teams are generally more careful in selecting loans by a loan's geo-location, a borrower's gender, a field partner's reliability, etc., when compared to lenders without team affiliations. In addition, we identified interesting lending behaviors of different lending teams based on lenders' background and interest such as their ethnic, religious, linguistic, educational, regional, and occupational aspects. Finally, using our proposed model, we tackled a novel problem of lending team recommendation and showed its promising performance results.