KLAN: Kuaishou Landing-page Adaptive Navigator

2025-07-31

AI Summary

This paper addresses the lack of personalized first-screen landing page recommendation in multi-page online platforms. We formally define the Personalized Landing Page Modeling (PLPM) task: selecting the optimal landing page from a candidate set upon app launch, jointly optimizing short-term conversion (Page-Dwell Rate, PDR), long-term user engagement (Daily Active Users, DAU; Lifetime, LT), and industrial constraints. To this end, we propose KLAN—a hierarchical adaptive framework comprising three components: KLAN-ISP models cross-day static page preferences; KLAN-IIT captures intra-day dynamic interest shifts; and KLAN-AM dynamically fuses these representations and performs navigation decision-making. Deployed at scale on Kuaishou, KLAN yields statistically significant improvements: +0.205% DAU and +0.192% LT, serving hundreds of millions of users. To our knowledge, this is the first work to formalize and solve PLPM in production-scale mobile applications.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Algorithm Configuration

Application Category

• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

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.

Problem

• Optimize personalized landing page selection for user engagement
• Improve page navigation in two-stage interaction paradigm
• Balance short-term metrics and long-term user satisfaction

Innovation

• Hierarchical framework for personalized landing pages
• Captures inter-day static page preferences
• Adaptively integrates dynamic interest transitions