PreferRec: 学习和迁移多目标重排序的帕累托偏好
PreferRec: Learning and Transferring Pareto Preferences for Multi-objective Re-ranking
AI总结：
PreferRec通过建模和迁移帕累托偏好提升多目标重排序性能，解决传统方法在用户偏好建模和跨用户知识迁移中的不足。
摘要：
多目标重排序已成为现代多阶段推荐系统的关键组件，其任务是平衡多个冲突目标，如准确性、多样性与公平性。现有方法通常在物品层面使用静态或手工制定的偏好权重优化聚合目标。这种设计忽略了用户在意图层面本质上表现出帕累托最优偏好，反映个性化目标之间的权衡而非固定权重组合。此外，大多数方法将每个用户的重排序任务视为孤立问题，反复从头学习偏好。这种范式不仅导致计算成本高，还忽略了用户在不同目标间共享相似偏好权衡结构的事实。受同质多目标优化空间中帕累托最优模式可迁移性的启发，我们提出了PreferRec，一个新型框架，旨在显式建模和迁移用户间的帕累托偏好。具体而言，PreferRec基于三个紧密耦合的组件：偏好感知的帕累托学习旨在在意图层面捕捉用户在多个冲突目标间的内在权衡。通过从重排序群体中学习帕累托偏好表示，该组件显式建模用户在不同上下文中如何优先不同目标。知识引导的迁移通过在同质优化空间中提取共享优化模式，实现高效的跨用户知识迁移。转移的知识随后用于指导解决方案选择和个性化重排序，使优化过程偏向帕累托前沿的高质量区域，同时保持用户特定的偏好特征。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
