论文标题
您是冒险者吗？对风险容忍度预测的不对称跨域一致性的对抗性学习
Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
论文作者
论文摘要
当前关于调查分析和风险公差建模的研究缺乏专业知识和领域特异性模型。鉴于生成对抗性学习在跨域信息中的有效性，我们为域量表不平等设计了一个不对称的跨域生成对抗网络（ADGAN）。 Adgan利用信息充足的域来提供额外的信息，以通过域的对齐方式改善有关信息不足域的表示。我们为两个数据源提供数据分析和用户模型：消费者消费信息和调查信息。我们进一步在具有视图嵌入结构的现实世界数据集上测试Adgan，并表明Adgan可以更好地处理类不平衡和不合格的数据空间，这表明了利用不对称域信息的有效性。
Most current studies on survey analysis and risk tolerance modelling lack professional knowledge and domain-specific models. Given the effectiveness of generative adversarial learning in cross-domain information, we design an Asymmetric cross-Domain Generative Adversarial Network (ADGAN) for domain scale inequality. ADGAN utilizes the information-sufficient domain to provide extra information to improve the representation learning on the information-insufficient domain via domain alignment. We provide data analysis and user model on two data sources: Consumer Consumption Information and Survey Information. We further test ADGAN on a real-world dataset with view embedding structures and show ADGAN can better deal with the class imbalance and unqualified data space than state-of-the-art, demonstrating the effectiveness of leveraging asymmetrical domain information.
