用多阶段检索增强金融推荐，让小模型也能精准匹配用户行为。
Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations
- 分两阶段并行检索用户交易与市场数据，构建精简知识图谱
- 在真实数据上提升推荐盈利性与行为一致性，小模型表现接近大模型
- 适合资源受限场景下的可落地金融AI系统
大型语言模型（LLMs）在个性化金融推荐中前景广阔，但受限于上下文长度、幻觉问题及缺乏行为依据。我们此前提出的FLARKO通过将结构化知识图谱（KG）嵌入提示词，使建议与用户行为和市场数据对齐。本文提出RAG-FLARKO，一种基于检索增强的FLARKO扩展，采用多阶段与并行知识图谱检索机制，解决可扩展性与相关性挑战。方法首先从用户交易知识图谱中检索行为相关的实体，再利用该上下文筛选时间一致的市场信号，构建紧凑且具根基的子图供LLM使用。该流程降低上下文开销，提升模型聚焦度。在真实金融交易数据集上的实证评估表明，RAG-FLARKO显著提升推荐质量。尤为关键的是，该框架使小型高效模型在盈利能力与行为一致性上达到高水准，为资源受限环境中的可落地金融AI提供可行路径。
原文摘要 · Abstract (English)
Large language models (LLMs) show promise for personalized financial recommendations but are hampered by context limits, hallucinations, and a lack of behavioral grounding. Our prior work, FLARKO, embedded structured knowledge graphs (KGs) in LLM prompts to align advice with user behavior and market data. This paper introduces RAG-FLARKO, a retrieval-augmented extension to FLARKO, that overcomes scalability and relevance challenges using multi-stage and parallel KG retrieval processes. Our method first retrieves behaviorally relevant entities from a user's transaction KG and then uses this context to filter temporally consistent signals from a market KG, constructing a compact, grounded subgraph for the LLM. This pipeline reduces context overhead and sharpens the model's focus on relevant information. Empirical evaluation on a real-world financial transaction dataset demonstrates that RAG-FLARKO significantly enhances recommendation quality. Notably, our framework enables smaller, more efficient models to achieve high performance in both profitability and behavioral alignment, presenting a viable path for deploying grounded financial AI in resource-constrained environments.
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