一篮子多专家：面向金融资产推荐的去相关重排序
原标题：One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation
AI 摘要
该论文提出 DSER（Decorrelated-Satellite Expert Re-ranking）重排序方法，用于金融资产推荐：保留专家跟随列表中最相关的 k-m 个资产作为相关性核心，将剩余 m 个槽位替换为与已选资产平均收益相关性最低的资产。在公开的 FAR-Trans 零售经纪数据集上，替换少量槽位可在小幅损失相关性的情况下提升风险调整后收益，且该收益在计入真实交易成本后仍然成立。作者强调 m 是服务商可显式设置、可审计的单一整数，使相关性与组合表现之间的权衡成为透明选择，而非隐藏在模型内部。
正文节选
One Basket, Many Experts: Decorrelated Re-ranking for Financial Asset Recommendation Abstract. Financial asset recommendation for retail investors is typically optimized for relevance, that is, how well a recommendation matches what each user will buy. From the service provider’s point of view, the value of a recommendation also depends on portfolio performance: the risk-adjusted return the recommended assets (the basket) deliver, which drives user engagement and fee revenue. The two goals are