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• W2500684018 abstract "Social networks contain a wealth of useful information. In this paper, we study a challenging task for integrating users' information from multiple heterogeneous social networks to gain a comprehensive understanding of users' interests and behaviors. Although much effort has been dedicated to study this problem, most existing approaches adopt linear or shallow models to fuse information from multiple sources. Such approaches cannot properly capture the complex nature of and relationships among different social networks. Adopting deep learning approaches to learning a joint representation can better capture the complexity, but this neglects measuring the level of confidence in each source and the consistency among different sources. In this paper, we present a framework for multiple social network learning, whose core is a novel model that fuses social networks using deep learning with source confidence and consistency regularization. To evaluate the model, we apply it to predict individuals' tendency to volunteerism. With extensive experimental evaluations, we demonstrate the effectiveness of our model, which outperforms several state-of-the-art approaches in terms of precision, recall and F1-score." @default.
• W2500684018 created "2016-08-23" @default.
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• W2500684018 date "2016-02-21" @default.
• W2500684018 modified "2023-10-16" @default.
• W2500684018 title "Fusing Social Networks with Deep Learning for Volunteerism Tendency Prediction" @default.
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• W2500684018 doi "https://doi.org/10.1609/aaai.v30i1.9985" @default.
• W2500684018 hasPublicationYear "2016" @default.
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