Authors:
William Johnny Bernardes de Oliveira
and
Wladmir Cardoso Brandão
Affiliation:
Department of Computer Science, Pontifical Catholic University of Minas Gerais (PUC Minas), Belo Hozizonte, Brazil
Keyword(s):
Recommendation Systems, Recommender, Machine Learning, Supervised Learning, Sponsorship, Social Project.
Abstract:
Non-government organizations play an important role in society, providing access to basic services in culture, education, health, and security for needy people. Some of these organizations raise funds for their social projects through sponsorship programs for people in poverty, deprivation, exclusion and vulnerability. The intensive use of technology for sponsors and beneficiaries matching is paramount to create more lasting bonds, maximizing the likelihood of stronger relationships, consequently raising more resources for projects. In this article we propose and evaluate a learning approach to recommend beneficiaries to sponsors. Particularly, we exploit different recommendation strategies, such as collaborative filtering with matrix factorization, content-based with bag of words and word embeddings and knowledge-based with association rules. Experimental results show that content-based strategies based on word embeddings are more effective, reaching up to 72% of performance in MAP
and nDCG. Additionally, it can effectively recommend beneficiaries to sponsors even if there is less feedback information on beneficiaries and sponsors to train recommendation models.
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