FinTRec：基于Transformer的金融应用情境化广告定向与个性化统一框架

FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications

November 18, 2025

作者: Dwipam Katariya, Snehita Varma, Akshat Shreemali, Benjamin Wu, Kalanand Mishra, Pranab Mohanty

cs.AI

摘要

基于Transformer的架构在序列推荐系统中已被广泛采用，但其在金融服务领域的实时推荐应用仍面临独特的实践与建模挑战。这些挑战包括：a) 用户跨数字与实体渠道产生的长周期交互行为（隐式与显式）会形成时间异质性上下文；b)多类关联产品并存需协调建模以支持多样化广告投放与个性化信息流，同时平衡相互竞争的业务目标。我们提出FinTRec这一基于Transformer的框架，旨在解决金融服务领域的这些挑战及运营目标。尽管传统上树模型因可解释性及符合监管要求更受金融领域青睐，但本研究证明FinTRec为转向基于Transformer的架构提供了可行有效的路径。通过历史模拟和线上A/B测试关联分析，我们表明FinTRec持续优于生产级树模型基线。该统一架构经过产品适配微调后，可实现跨产品信号共享，降低训练成本与技术负债，同时提升所有产品的离线性能。据我们所知，这是首个在金融服务领域兼顾技术考量与业务需求的统一序列推荐建模综合性研究。

English

Transformer-based architectures are widely adopted in sequential recommendation systems, yet their application in Financial Services (FS) presents distinct practical and modeling challenges for real-time recommendation. These include:a) long-range user interactions (implicit and explicit) spanning both digital and physical channels generating temporally heterogeneous context, b) the presence of multiple interrelated products require coordinated models to support varied ad placements and personalized feeds, while balancing competing business goals. We propose FinTRec, a transformer-based framework that addresses these challenges and its operational objectives in FS. While tree-based models have traditionally been preferred in FS due to their explainability and alignment with regulatory requirements, our study demonstrate that FinTRec offers a viable and effective shift toward transformer-based architectures. Through historic simulation and live A/B test correlations, we show FinTRec consistently outperforms the production-grade tree-based baseline. The unified architecture, when fine-tuned for product adaptation, enables cross-product signal sharing, reduces training cost and technical debt, while improving offline performance across all products. To our knowledge, this is the first comprehensive study of unified sequential recommendation modeling in FS that addresses both technical and business considerations.