用机器学习+检索增强生成，让金融营销更精准合规。
Hybrid Intent-Aware Personalization with Machine Learning and RAG-Enabled Large Language Models for Financial Services Marketing
- 结合传统模型与RAG大模型，分步预测用户意图和推荐内容。
- 时间建模和意图特征提升个性化准确率，检索减少错误生成。
- 适合需要可解释、可审计的金融营销系统开发者。
金融领域个性化营销需兼顾客户行为预测与合规内容生成。本文提出一种混合架构：前段用经典机器学习进行用户分群、隐含意图建模与个性化推荐预测；后段采用检索增强生成（RAG）技术，基于领域文档生成合规且上下文相关的客户沟通内容。研究构建了一个合成的、可复现的数据集，模拟客户随时间的行为、产品互动及营销响应。框架包含时间编码器、潜在表示和多任务分类器，用于估计用户所属群体、意图及产品-渠道推荐。随后的RAG生成层依据检索到的领域文档生成客户消息。实验表明，时间建模与意图特征显著提升个性化精度，基于引用的检索有效降低非支持性生成，并增强在监管环境下的可审计性。核心贡献为架构设计，展示了预测建模与RAG生成如何整合成透明、可解释的金融个性化流程。
原文摘要 · Abstract (English)
Personalized marketing in financial services requires models that can both predict customer behavior and generate compliant, context-appropriate content. This paper presents a hybrid architecture that integrates classical machine learning for segmentation, latent intent modeling, and personalization prediction with retrieval-augmented large language models for grounded content generation. A synthetic, reproducible dataset is constructed to reflect temporal customer behavior, product interactions, and marketing responses. The proposed framework incorporates temporal encoders, latent representations, and multi-task classification to estimate segment membership, customer intent, and product-channel recommendations. A retrieval-augmented generation layer then produces customer-facing messages constrained by retrieved domain documents. Experiments show that temporal modeling and intent features improve personalization accuracy, while citation-based retrieval reduces unsupported generation and supports auditability in regulated settings. The contribution is primarily architectural, demonstrating how predictive modeling and RAG-based generation can be combined into a transparent, explainable pipeline for financial services personalization.
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