用行为金融学优化大模型，让投资建议更符合人性且赚钱。
Aligning Language Models with Investor and Market Behavior for Financial Recommendations
- 用知识图谱结构化用户交易与资产趋势，增强可解释性。
- 在FAR-Trans数据集上，行为对齐度和收益均优于现有方法。
- 首次结合行为优化与联邦学习，适合注重隐私的金融场景。
多数金融推荐系统忽视关键行为与监管因素，导致建议与用户偏好不符、难以理解或难以执行。本文提出FLARKO（金融语言模型资产推荐知识图谱优化），融合大语言模型（LLM）、知识图谱（KG）与卡尼曼-特沃斯基优化（KTO），生成既盈利又符合行为特征的资产推荐。FLARKO将用户交易历史与资产趋势编码为结构化知识图谱，为大模型提供可解释、可控制的上下文。为验证方法适应性，构建了集中式（CenFLARKO）与联邦变体（FedFLARKO）。据我们所知，这是首次将KTO用于大模型微调以实现金融资产推荐，也是首次在联邦学习（FL）环境下使用结构化知识图谱引导大模型对行为金融数据进行推理。在FAR-Trans数据集上的评估显示，FLARKO在行为对齐与联合收益上持续优于前沿推荐基线，同时保持可解释性与资源效率。
原文摘要 · Abstract (English)
Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to interpret, or unlikely to be followed. We present FLARKO (Financial Language-model for Asset Recommendation with Knowledge-graph Optimization), a novel framework that integrates Large Language Models (LLMs), Knowledge Graphs (KGs), and Kahneman-Tversky Optimization (KTO) to generate asset recommendations that are both profitable and behaviorally aligned. FLARKO encodes users' transaction histories and asset trends as structured KGs, providing interpretable and controllable context for the LLM. To demonstrate the adaptability of our approach, we develop and evaluate both a centralized architecture (CenFLARKO) and a federated variant (FedFLARKO). To our knowledge, this is the first demonstration of combining KTO for fine-tuning of LLMs for financial asset recommendation. We also present the first use of structured KGs to ground LLM reasoning over behavioral financial data in a federated learning (FL) setting. Evaluated on the FAR-Trans dataset, FLARKO consistently outperforms state-of-the-art recommendation baselines on behavioral alignment and joint profitability, while remaining interpretable and resource-efficient.
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