Multi-objective re-ranking has become a critical component of modern multi-stage recommender systems, as it tasked to balance multiple conflicting objectives such as accuracy, diversity, and fairness. Existing multi-objective re-ranking methods typically optimize aggregate objectives at the item level using static or handcrafted preference weights. This design overlooks that users inherently exhibit Pareto-optimal preferences at the intent level, reflecting personalized trade-offs among objectives rather than fixed weight combinations. Moreover, most approaches treat re-ranking task for each user as an isolated problem, and repeatedly learn the preferences from scratch. Such a paradigm not only incurs high computational cost, but also ignores the fact that users often share similar preference trade-off structures across objectives. Inspired by the existence of homogeneous multi-objective optimization spaces where Pareto-optimal patterns are transferable, we propose PreferRec, a novel framework that explicitly models and transfers Pareto preferences across users. Specifically, PreferRec is built upon three tightly coupled components: Preference-Aware Pareto Learning aims to capture user intrinsic trade-offs among multiple conflicting objectives at the intent level. By learning Pareto preference representations from re-ranking populations, this component explicitly models how users prioritize different objectives under diverse contexts. Knowledge-Guided Transfer facilitates efficient cross-user knowledge transfer by distilling shared optimization patterns across homogeneous optimization spaces. The transferred knowledge is then used to guide solution selection and personalized re-ranking, biasing the optimization process toward high-quality regions of the Pareto front while preserving user-specific preference characteristics.

翻译：多目标重排已成为现代多阶段推荐系统的关键组成部分，其任务在于平衡准确性、多样性和公平性等多个相互冲突的目标。现有方法通常在物品层级使用静态或人工设计的偏好权重来优化聚合目标。这种设计忽略了用户在意图层级天然存在的帕累托最优偏好，这些偏好体现了目标间个性化的权衡关系，而非固定的权重组合。此外，多数方法将每个用户的重排任务视为孤立问题，从头重复学习偏好，不仅导致高计算成本，也忽视了用户间常共享相似的目标偏好权衡结构。受同质多目标优化空间中帕累托最优模式可迁移的启发，我们提出PreferRec框架，显式建模并跨用户迁移帕累托偏好。具体而言，PreferRec由三个紧密耦合的组件构成：偏好感知帕累托学习旨在意图层级捕捉用户对多个冲突目标的内在权衡，通过从重排群体中学习帕累托偏好表征，显式建模用户在不同背景下对各目标的优先级排序；知识引导迁移通过蒸馏同质优化空间中的共享优化模式，实现高效的跨用户知识迁移，迁移后的知识被用于指导解选择与个性化重排，将优化过程导向帕累托前沿的高质量区域，同时保留用户专属偏好特征。