用序列建模优化个性化首页推荐，提升用户留存与活跃度。
From Bootstrapping to Sequence Modeling: A Unified Generative Framework for Personalized Landing-Page Modeling
- 基于决策Transformer构建统一框架，兼顾全局与局部行为建模。
- 在线实验显示日活提升0.158%，用户生命周期延长0.108%。
- 适合关注推荐系统长期用户行为建模的研究者与工程师。
现代在线平台普遍采用多页面架构以满足多样化用户需求。页面导航作为应用启动后的关键入口，直接影响用户首印象并显著影响后续参与度。为优化该过程，快手提出个性化首页建模（PLPM）任务，并设计了基于保守Q学习（CQL）的KLAN强化学习框架。然而，基于CQL的方法存在两大局限：一是马尔可夫假设无法捕捉真实用户行为中的强非马尔可夫时序依赖；二是带自举的TD学习在延迟奖励场景下累积误差严重，尤其在用户每日多次进入的长周期设置中面临信用分配难题。为此，我们提出GLAN（生成式首页自适应导航器），一个基于决策Transformer的序列建模框架，从全局-局部统一视角解决PLPM问题。具体地，设计了L-RTG模块以捕获跨日消费动态，为当日所有页面分配提供精准全局指导；同时提出HRM模块，将会话级反馈分解为细粒度信号，实现对每页分配的精确局部监督。在快手平台上的大规模在线实验表明，GLAN在日活跃用户（DAU）上提升0.158%，用户生命周期（LT）提升0.108%。
原文摘要 · Abstract (English)
Modern online platforms increasingly adopt multi-page architectures to accommodate diverse user needs. On these platforms, page navigation (the process of directing users to specific functional pages upon app entry) serves as a critical gateway that shapes user's first impression and significantly influences subsequent engagement. To optimize this process, Kuaishou formulated the task of Personalized Landing Page Modeling (PLPM) and proposed KLAN, a reinforcement learning framework built upon Conservative Q-Learning (CQL). However, CQL-based approaches suffer from two fundamental limitations: (1) the Markov assumption fails to capture the strong non-Markovian temporal dependencies inherent in real-world user behaviors, and (2) TD learning with bootstrapping incurs severe cumulative errors and credit assignment difficulties under delayed rewards, particularly in long-horizon settings where users enter the app multiple times daily. To address these limitations, we propose GLAN (Generative Landing-page Adaptive Navigator), a sequence modeling framework built on Decision Transformer to tackle PLPM from a unified global-local perspective. Specifically, GLAN incorporates two key modules. First, we design the L-RTG module that captures users' inter-day consumption dynamics to provide accurate global guidance for all page assignments within a day. Furthermore, we propose the HRM module that decomposes session-level feedback into fine-grained signals, enabling precise local supervision for each page assignment. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of GLAN, achieving +0.158\% and +0.108\% improvements on Daily Active Users (DAU) and user Lifetime (LT) respectively.
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