标题： 与投资者和市场行为对齐的语言模型用于金融建议 显示英文标题

标题： Aligning Language Models with Investor and Market Behavior for Financial Recommendations

摘要： 大多数金融推荐系统往往无法考虑关键的行为和监管因素，导致建议与用户偏好不一致、难以理解或不太可能被遵循。 我们提出了FLARKO（用于资产推荐的金融语言模型与知识图谱优化），这是一种新颖的框架，结合了大型语言模型（LLMs）、知识图谱（KGs）和Kahneman-Tversky优化（KTO），生成既有利可图又符合行为的资产推荐。 FLARKO将用户的交易历史和资产趋势编码为结构化的KGs，为LLM提供可解释且可控的上下文。 为了展示我们方法的适应性，我们开发并评估了一个集中式架构（CenFLARKO）和一个联邦变体（FedFLARKO）。 据我们所知，这是首次展示将KTO用于金融资产推荐的LLMs微调。 我们还首次在联邦学习（FL）环境中使用结构化KGs来使LLM在行为金融数据上的推理具有基础。 在FAR-Trans数据集上评估，FLARKO在行为一致性与联合盈利能力方面始终优于最先进的推荐基线，同时保持可解释性和资源效率。 显示英文摘要

摘要： 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.