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.

中文翻译：

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人口预测的深度能量分解模型

人口统计信息对于各种商业和学术建议很重要，但实际上，这些数据中很少有可用于分析和研究的。为了解决这个问题，一些研究从用户的行为数据中预测人口统计属性。然而，以前的作品有不同的缺点。处理数据稀疏性和定义有用的特征仍然是特别具有挑战性的任务。在这篇文章中，我们提出了一部小说深度能量分解模型来解决这两个缺点。该模型是一个设计好的网络，可以执行多标签分类和特征表示。在具有四个评估指标的四个数据集上进行实验。实证结果表明，我们的深度能量分解模型明显优于最先进的模型。