基于大语言模型的智能通知生成：从静态个性化到情境感知的说服性信息
LLM-Based Intelligent Notification Composition: From Static Personalization to Context-Aware Persuasive Messaging
AI总结：
本文提出利用大语言模型提升通知信息质量，通过六个维度评估其效果，展示LLM在提升CTR和说服性方面的贡献，并提出决策框架以指导LLM生成的应用。
摘要：
推送通知仍然是数字平台与用户互动最直接的渠道，但现有方法在通知对象、时间及内容推荐上投入巨大，而如何沟通仍是最薄弱的环节。本文认为信息质量是独立且被低估的优化点，大语言模型在此层创造最大差异化价值。本文贡献包括：首先定义通知信息质量的六个维度，并展示基于LLM的生成如何优于模板；其次提供架构归因分析，解构信息生成与其他组件（目标定位、排序、时间）的关系；第三引入三准则决策框架，明确何时LLM生成是瓶颈。本文通过PRISMA引导的调研（28个来源，142个筛选）、社交媒体、食品配送和电子商务领域的应用分析，并提出统一的架构框架，包含预算感知路由、基础生成、候选排序、多样性控制和在线学习。
英文摘要：
Push notifications remain among the most direct channels through which digital platforms engage users, yet existing approaches have invested heavily in who to notify, when to notify, and what to recommend, while leaving how to communicate as the least-optimized stage. This paper argues that message quality is an independent, underinvested lever, and that LLMs create their most differentiated value precisely at this layer. We make three contributions. First, we define notification message quality along six dimensions (contextual relevance, clarity, actionability, novelty handling, linguistic freshness, and persuasive appropriateness) and show how LLM-based composition improves each relative to templates. Across reviewed deployments, reported improvements range from +8% to +14.5% CTR over static templates and +1% to +2.5% over mature slot-filling systems, though these span heterogeneous systems and should not be treated as directly comparable. Second, we provide an architectural attribution analysis disentangling message generation from adjacent components (targeting, ranking, timing), arguing that observed gains are frequently misattributed to text generation alone. Third, we introduce a three-criterion decision framework specifying when LLM generation is and is not the binding constraint. We support these arguments through a PRISMA-guided survey (28 sources from 142 screened), examine domain-specific applications across social media, food delivery, and e-commerce, and propose a unified architectural framework with budget-aware routing, grounded generation, candidate ranking, diversity controls, and online learning.
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。
