FinTRec：面向金融应用的基于Transformer的统一上下文广告定向与个性化框架
FinTRec: Transformer Based Unified Contextual Ads Targeting and Personalization for Financial Applications
AI总结：
针对金融服务实时推荐的特殊挑战，提出基于Transformer的统一框架FinTRec，经实验验证其性能优于生产级树模型基线，可降低成本并支持跨产品信号共享。
摘要：
基于Transformer的架构已被序列推荐系统广泛采用，但其在金融服务（FS）领域的实时推荐应用面临着独特的实践与建模挑战，具体包括：a）跨越数字与实体渠道的长程用户交互（隐式与显式）会产生时间异质性上下文；b）存在多种相互关联的产品，需要协调模型来支持多样的广告位与个性化信息流，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，以解决上述挑战及金融服务领域的运营目标。尽管树模型因可解释性且符合监管要求，历来是金融服务领域的首选方案，但我们的研究表明，FinTRec为向基于Transformer的架构转型提供了可行且有效的路径。通过历史模拟与线上A/B测试相关性验证，我们证明FinTRec的表现始终优于生产级树模型基线。该统一架构经微调适配不同产品后，可实现跨产品信号共享，降低训练成本与技术债务，同时提升所有产品的离线性能。据我们所知，这是金融服务领域首个同时兼顾技术与业务考量的统一序列推荐建模综合研究。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
