AI 中文总结
研究金融资产推荐中如何兼顾投资回报与偏好匹配的问题，提出专家跟随策略框架，通过识别顶级投资者并按特定方式推荐其购买资产，实验证明该策略在ROI和nDCG上显著优于市场平均基线。
AI 中文摘要
金融机构拥有丰富交易历史，但要给出能同时最大化投资回报并确保偏好匹配的推荐仍是重大挑战。现有基于回报和基于偏好的策略各优化单一目标，导致盈利性（ROI）和相关性（nDCG）间的基本权衡。本文提出专家跟随策略框架，基于历史ROI识别顶级投资者并推荐其购买资产，按ROI加权购买频率评分。实验表明该策略在所有四个阈值下的ROI和nDCG均显著优于市场平均基线。
英文摘要
Financial institutions hold rich transaction histories, yet delivering recommendations that simultaneously maximize investment returns and ensure preference alignment remains a significant challenge. Existing approaches, namely return-based and preference-based strategies, each optimize a single objective, resulting in a fundamental trade-off between profitability (ROI) and relevance (nDCG). In this paper, we propose the Expert-Following Strategies: a framework that identifies top-performing investors based on their historical ROI and recommends the assets they purchased, scored by ROI-weighted purchase frequency. Our experiments using real-world transaction histories show that our strategy achieves statistically significant improvement over the market-average baseline in both ROI and nDCG simultaneously across all four thresholds.
Comments2pages, 1figure
