Affiliation:
1. National Taiwan University, Taipei, Taiwan
2. National Cheng Kung University, Tainan, Taiwan
Abstract
Demographic information is important for various commercial and academic proposes, but in reality, few of these data are accessible for analysis and research. To solve this problem, several studies predict demographic attributes from users’ behavioral data. However, previous works suffer from different kinds of disadvantages. Handling data sparseness and defining useful features remain especially challenge tasks. In this article, we propose a novel
Deep Energy Factorization Model
to address these two drawbacks. The model is a designed network that performs multi-label classification and feature representation. Experiments are conducted on four datasets with four evaluation metrics. The empirical results show that our Deep Energy Factorization Model significantly outperforms state-of-the-art models.
Funder
Taiwan Ministry of Science and Technology
Academia Sinica
Publisher
Association for Computing Machinery (ACM)
Subject
Artificial Intelligence,Theoretical Computer Science
Cited by
4 articles.
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