KLAN: Kuaishou Landing-page Adaptive Navigator
Fan Li, Chang Meng, Jiaqi Fu, Shuchang Liu, Jiashuo Zhang, Tianke Zhang, Xueliang Wang, Xiaoqiang Feng

TL;DR
This paper introduces KLAN, a hierarchical framework for personalized landing page selection in multi-page online platforms, significantly improving user engagement and satisfaction through optimized navigation.
Contribution
The paper formalizes the task of Personalized Landing Page Modeling and proposes KLAN, a novel hierarchical solution framework for proactive, personalized page navigation in recommender systems.
Findings
Online experiments show +0.205% DAU improvement.
Online experiments show +0.192% user lifetime increase.
Deployed at full traffic on Kuaishou, serving hundreds of millions of users.
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…
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Taxonomy
TopicsInertial Sensor and Navigation
