标题： KLAN：快手落地页自适应导航器 显示英文标题

标题： KLAN: Kuaishou Landing-page Adaptive Navigator

摘要： 现代在线平台配置多个页面以满足多样化用户需求。 这种多页面架构在用户和平台之间固有地建立了一个两阶段的交互范式：(1) 第一阶段：页面导航，将用户引导至特定页面；(2) 第二阶段：页面内交互，用户在特定页面中与定制化内容进行互动。 尽管大多数研究集中在改进第二阶段用户反馈的顺序推荐任务上，但对如何在第一阶段实现更好的页面导航的研究却很少。 为了填补这一空白，我们将个性化着陆页建模（PLPM）任务正式定义为推荐系统领域的问题：在用户进入应用程序时，PLPM的目标是从一组候选页面（例如，功能标签、内容频道或聚合页面）中主动选择最合适的着陆页，以优化短期PDR指标和长期用户参与度和满意度指标，同时遵守工业约束。 此外，我们提出了KLAN（快手着陆页自适应导航器），这是一个分层解决方案框架，旨在在PLPM的框架下提供个性化的着陆页。 KLAN包括三个关键组件：(1) KLAN-ISP捕捉跨天静态页面偏好；(2) KLAN-IIT捕捉当天动态兴趣转换；(3) KLAN-AM自适应地整合这两个组件以做出最佳导航决策。 在快手平台上进行的大量在线实验证明了KLAN的有效性，在每日活跃用户（DAU）和用户生命周期（LT）上分别获得了+0.205%和+0.192%的提升。 我们的KLAN最终在在线平台上全流量部署，服务于数亿用户。 为了促进这一重要领域的进一步研究，我们将在论文接受后发布我们的数据集和代码。 显示英文摘要

摘要： 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.