提出可扩展可解释的智能推荐框架，提升动态促销场景下的个性化决策效率。
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype
- 以产品类别为上下文建模，支持跨类知识迁移，提升学习效率。
- 融合会员购买缺口与矩阵分解特征，实现对用户-优惠互动的精细捕捉。
- 通过逻辑回归+大模型接口，实现可解释的实时偏好追踪，适合落地部署。
本文综述了上下文多臂老虎机（CMAB）方法，并提出一个面向快速变化促销场景的可扩展、可解释的优惠选择实验框架。该方法在产品类别层面建模上下文，使优惠可覆盖多个类别，促进相似优惠间的知识迁移，从而提升动态环境中的学习效率与泛化能力。框架通过高效的特征工程与模块化设计实现可扩展性，引入MPG（会员购买缺口）和MF（矩阵分解）等高级特征，捕捉用户与优惠之间的细微交互。采用Python实现，支持实际部署。关键贡献在于大规模可解释性：基于逻辑回归的权重向量可通过大语言模型（LLM）接口实时访问，实现用户级偏好追踪与决策解释，生成详细会员画像并识别行为模式，助力个性化优惠优化，增强自动化决策的信任度。将原型置于广义线性模型与Thompson采样等经典范式中对比，验证其在研究与实际应用中的价值。
原文摘要 · Abstract (English)
This paper presents a concise review of Contextual Multi-Armed Bandit (CMAB) methods and introduces an experimental framework for scalable, interpretable offer selection, addressing the challenge of fast-changing offers. The approach models context at the product category level, allowing offers to span multiple categories and enabling knowledge transfer across similar offers. This improves learning efficiency and generalization in dynamic environments. The framework extends standard CMAB methodology to support multi-category contexts, and achieves scalability through efficient feature engineering and modular design. Advanced features such as MPG (Member Purchase Gap) and MF (Matrix Factorization) capture nuanced user-offer interactions, with implementation in Python for practical deployment. A key contribution is interpretability at scale: logistic regression models yield transparent weight vectors, accessible via a large language model (LLM) interface for real-time, user-level tracking and explanation of evolving preferences. This enables the generation of detailed member profiles and identification of behavioral patterns, supporting personalized offer optimization and enhancing trust in automated decisions. By situating our prototype alongside established paradigms like Generalized Linear Models and Thompson Sampling, we demonstrate its value for both research and real-world CMAB applications.
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