Information filtering and information retrieval: two sides of the same coin?
Communications of the ACM - Special issue on information filtering
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Modern Information Retrieval
Document clustering based on non-negative matrix factorization
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
IEEE Transactions on Knowledge and Data Engineering
Data Fusion and Multicue Data Matching by Diffusion Maps
IEEE Transactions on Pattern Analysis and Machine Intelligence
Cross-language information retrieval using PARAFAC2
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning multiple graphs for document recommendations
Proceedings of the 17th international conference on World Wide Web
Manifold alignment using Procrustes analysis
Proceedings of the 25th international conference on Machine learning
Introduction to Information Retrieval
Introduction to Information Retrieval
Relational learning via collective matrix factorization
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Mind the gaps: weighting the unknown in large-scale one-class collaborative filtering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
Relevance and ranking in online dating systems
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Collaborative competitive filtering: learning recommender using context of user choice
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
"I loan because...": understanding motivations for pro-social lending
Proceedings of the fifth ACM international conference on Web search and data mining
Proceedings of the 23rd international conference on World wide web
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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.