As a popular e-commerce platform, Kuaishou E-shop provides precise personalized product recommendations to tens of millions of users every day. To better respond real-time user feedback, we have deployed an interactive recommender system (IRS) alongside our core homepage recommender system. This IRS is triggered by user click on homepage, and generates a series of highly relevant recommendations based on the clicked item to meet focused browsing demands. Different from traditional e-commerce RecSys, the full-screen UI and immersive swiping down functionality present two distinct challenges for regular ranking system. First, there exists explicit interference (overlap or conflicts) between ranking objectives, i.e., conversion, view and swipe down. This is because there are intrinsic behavioral co-occurrences under the premise of immersive browsing and swiping down functionality. Second, the ranking system is prone to temporal greedy traps in sequential recommendation slot transitions, which is caused by full-screen UI design. To alleviate these challenges, we propose a novel Spatio-temporal collaborative ranking (STCRank) framework to achieve collaboration between multi-objectives within one slot (spatial) and between multiple sequential recommondation slots. In multi-objective collaboration (MOC) module, we push Pareto frontier by mitigating the objective overlaps and conflicts. In multi-slot collaboration (MSC) module, we achieve global optima on overall sequential slots by dual-stage look-ahead ranking mechanism. Extensive experiments demonstrate our proposed method brings about purchase and DAU co-growth. The proposed system has been already deployed at Kuaishou E-shop since 2025.6.

翻译：作为主流电商平台，快手电商每日为数千万用户提供精准的个性化商品推荐。为更好地响应用户实时反馈，我们在核心首页推荐系统之外部署了交互式推荐系统。该系统由用户点击首页商品触发，并基于被点击商品生成一系列高度相关的推荐，以满足聚焦浏览需求。与传统电商推荐系统不同，全屏界面与沉浸式下滑功能为常规排序系统带来两大挑战。其一，排序目标（即转化、浏览与下滑）之间存在显式干扰（重叠或冲突），这源于沉浸式浏览与下滑功能前提下固有的行为共现性。其二，在全屏界面设计下，排序系统在连续推荐位转换中易陷入时序贪婪陷阱。为缓解这些挑战，我们提出一种新颖的时空协同排序框架，实现单个推荐位内（空间维度）多目标之间以及连续推荐位之间（时间维度）的协同优化。在多目标协同模块中，我们通过缓解目标重叠与冲突来推进帕累托前沿；在多推荐位协同模块中，我们通过双阶段前瞻排序机制实现整体序列推荐位的全局最优。大量实验表明，所提方法能同时促进购买量与日活跃用户数的增长。该系统已于2025年6月在快手电商平台完成部署。