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 metric Page Drop-off Ratio (PDR) 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.

Online video recommendation systems often build binary labels based on play complete rate (i.e., the ratio of watch time to video duration), such as complete play and effective play, using them as implicit feedback for Click-Through Rate (CTR) prediction tasks to gauge user interest. Existing works tend to improve prediction accuracy by designing complex models, overlooking that a key cause of inaccurate predictions is the disorganization of instance representation space. To address this issue, we explore a novel approach using prototype learning to calibrate the instance representation space of deep recommendation models and propose a model-agnostic Contrastive Prototype Framework (CPF). Firstly, CPF partitions the instance space into different subspaces based on duration, then generates positive and negative prototype pairs for each subspace from pre-trained recommendation model. Subsequently, we map the instance representations to the prototype space and calibrate them by reducing the distance to the corresponding prototypes. Ultimately, the prediction is derived from the linear combination of the estimated values associated with each prototype. To prevent disorganization in the prototype space during training, we design contrastive and orthogonality losses to constrain the learning of prototypes. Additionally, we show that how CPF effectively addresses the duration bias from the perspective of causal intervention. Offline experiments on two datasets demonstrate that CPF improves recommendation accuracy over several baseline models in predicting five widely used implicit feedback labels. We have also deployed CPF on a short video platform, validating its effectiveness in real-world scenarios.