Deep Energy Factorization Model for Demographic Prediction
Chih-Te Lai, Cheng–Te Li, Shou-De Lin · ACM Transactions on Intelligent Systems and Technology · 2020
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.