Modern online platforms configure multiple pages to accommodate diverse user needs. This multi-page architecture inherently establishes a two-stage interaction paradigm between the user and the platform: (1) Stage I: page navigation, navigating users to a specific page and (2) Stage II: in-page interaction, where users engage with customized content within the specific page. While the majority of research has been focusing on the sequential recommendation task that improves users' feedback in Stage II, there has been little investigation on how to achieve better page navigation in Stage I. To fill this gap, we formally define the task of Personalized Landing Page Modeling (PLPM) into the field of recommender systems: Given a user upon app entry, the goal of PLPM is to proactively select the most suitable landing page from a set of candidates (e.g., functional tabs, content channels, or aggregation pages) to optimize the short-term PDR metric and the long-term user engagement and satisfaction metrics, while adhering to industrial constraints. Additionally, we propose KLAN (Kuaishou Landing-page Adaptive Navigator), a hierarchical solution framework designed to provide personalized landing pages under the formulation of PLPM. KLAN comprises three key components: (1) KLAN-ISP captures inter-day static page preference; (2) KLAN-IIT captures intra-day dynamic interest transitions and (3) KLAN-AM adaptively integrates both components for optimal navigation decisions. Extensive online experiments conducted on the Kuaishou platform demonstrate the effectiveness of KLAN, obtaining +0.205% and +0.192% improvements on in Daily Active Users (DAU) and user Lifetime (LT). Our KLAN is ultimately deployed on the online platform at full traffic, serving hundreds of millions of users. To promote further research in this important area, we will release our dataset and code upon paper acceptance.

翻译：现代在线平台通过配置多个页面来满足多样化的用户需求。这种多页面架构本质上建立了用户与平台之间的两阶段交互范式：（1）第一阶段：页面导航，引导用户进入特定页面；（2）第二阶段：页面内交互，用户在特定页面内与定制化内容进行互动。尽管现有研究大多聚焦于提升第二阶段用户反馈的序列推荐任务，但对于如何实现更优的第一阶段页面导航却鲜有探讨。为填补这一空白，我们正式将个性化落地页建模任务引入推荐系统领域：给定用户进入应用时的上下文，PLPM的目标是从候选页面集合中主动选择最合适的落地页，以优化短期PDR指标以及长期用户参与度和满意度指标，同时满足工业约束。此外，我们提出了KLAN——一个基于PLPM框架设计的、用于提供个性化落地页的分层解决方案。KLAN包含三个核心组件：（1）KLAN-ISP捕捉跨日静态页面偏好；（2）KLAN-IIT捕捉日内动态兴趣迁移；（3）KLAN-AM自适应整合两个组件以生成最优导航决策。在快手平台上进行的大规模在线实验证明了KLAN的有效性，其日活跃用户数和用户生命周期分别实现了+0.205%和+0.192%的提升。目前KLAN已在全流量线上平台部署，为数亿用户提供服务。为推进该重要领域的后续研究，我们将在论文录用后公开数据集和代码。