为快手首页导航设计个性化推荐系统，提升用户留存与活跃度
KLAN: Kuaishou Landing-page Adaptive Navigator
- 构建分层框架KLAN，融合用户长期偏好与短期兴趣变化
- 在线实验显示日活提升0.205%，用户生命周期延长0.192%
- 适合关注平台首页优化与推荐系统落地的工程师和研究者
现代在线平台通过多页面架构满足多样化用户需求，形成两阶段交互模式：第一阶段为页面导航，第二阶段为页面内互动。现有研究多聚焦于第二阶段的序列推荐，对第一阶段的页面导航关注不足。为此，本文首次将个性化首页建模（PLPM）正式引入推荐系统领域：给定用户进入应用时的状态，目标是从候选页面中主动选择最合适的首页，以优化短期点击率（PDR）及长期用户活跃与满意度，同时满足工业约束。为此提出KLAN（快手首页自适应导航器）框架，包含三个组件：KLAN-ISP捕捉跨日静态页面偏好，KLAN-IIT捕捉日内动态兴趣转移，KLAN-AM动态融合两者实现最优导航决策。在快手平台的广泛在线实验表明，KLAN使日活跃用户（DAU）提升0.205%，用户生命周期（LT）延长0.192%。该系统已全量部署，服务数亿用户。为促进该方向研究，论文接受后将公开数据集与代码。
原文摘要 · Abstract (English)
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.
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