社会经济领域的冷启动主动偏好学习

主动偏好学习提供了一种高效的建模偏好的方法，但受到冷启动问题的阻碍，这一问题在没有初始标记数据可用时会导致性能显著下降。虽然为视觉和文本等领域提出了冷启动解决方案，但社会经济领域的主动偏好学习冷启动问题仍基本未被探索，亟需实用且有效的方法。drawing inspiration from established practices in social and economic research, the proposed method initiates learning with a self-supervised phase that employs Principal Component Analysis (PCA) to generate initial pseudo-labels. This process produces a "warmed-up" model based solely on the data's intrinsic structure, without requiring expert input. The model is then refined through an active learning loop that strategically queries a simulated noisy oracle for labels. Experiments conducted on various socio-economic datasets, including those related to financial credibility, career success rate, and socio-economic status, consistently show that the PCA-driven approach outperforms standard active learning strategies that start without prior information. This work thus provides a computationally efficient and straightforward solution that effectively addresses the cold-start problem.