Are You A Risk Taker? Adversarial Learning of Asymmetric Cross-Domain Alignment for Risk Tolerance Prediction
This work addresses risk tolerance prediction for survey analysis and modeling, offering a domain-specific solution that is incremental in applying adversarial learning to cross-domain alignment.
The paper tackled the problem of predicting risk tolerance by addressing domain scale inequality between information-sufficient and information-insufficient domains, using an Asymmetric cross-Domain Generative Adversarial Network (ADGAN) that improved representation learning and outperformed state-of-the-art methods in handling class imbalance and unqualified data.
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.