  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.
