发表机构
Shanghai University of Finance and Economics; Singapore University of Technology and Design（上海财经大学; 新加坡科技设计大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出贡献-性能公平性及CPFR框架，通过对齐用户贡献与推荐性能，在保持公平的同时激励参与，实验证明其实现了强准确性-公平性权衡。
AI 中文摘要
现有关于推荐系统中用户公平性的研究已经发展了多样化的目标。然而，它很少关注一个独特的分配视角：用户对模型学习的贡献是否应反映在他们获得的推荐收益中。我们认为，除了现有的公平保护之外，一个公平的系统可以考虑用户估计贡献与他们获得的推荐性能之间的一致性。这种一致性可以激励持续且信息丰富的参与，从而支持可持续的推荐生态系统。为此，我们提出了贡献-性能公平性，这是一种新颖的公平视角，要求推荐性能在不同用户群体中与估计贡献对齐，并在同一群体内贡献相当的用户之间保持公平。为了实例化这一视角，我们引入了贡献-性能公平推荐器（CPFR），这是一个适用于不同骨干推荐器的框架。CPFR根据训练依赖的贡献构建有序用户群体，该贡献考虑交互量、损失对齐和优化强度，并联合优化推荐准确性与两个公平性要求。博弈论分析表明，这种对齐可以在自愿贡献下加强贡献激励并提高系统级推荐准确性。在三个数据集和三个骨干模型上的实验表明，CPFR在所提出的操作指标下实现了强准确性-公平性权衡。
英文摘要
Existing research on user fairness in recommender systems has developed diverse objectives. However, it has paid limited attention to a distinct distributive perspective: whether users' contributions to model learning should be reflected in the recommendation benefits they receive. We argue that, in addition to existing fairness protections, a fair system may account for the alignment between users' estimated contributions and the recommendation performance they receive. Such alignment can incentivize sustained and informative engagement, thereby supporting a sustainable recommendation ecosystem. To this end, we propose Contribution-Performance Fairness, a novel fairness perspective which requires recommendation performance to be aligned with estimated contribution across user groups and to remain equitable among users with comparable contributions within a same group. To instantiate this perspective, we introduce the Contribution-Performance Fair Recommender (CPFR), a framework applicable to different backbone recommenders. CPFR constructs ordered user groups from a training-dependent contribution considering interaction volume, loss alignment, and optimization intensity, and jointly optimizes recommendation accuracy with the two fairness requirements. A game-theoretic analysis shows that such alignment can strengthen contribution incentives and improve system-level recommendation accuracy under voluntary contribution. Experiments on three datasets and three backbone models demonstrate that CPFR achieves a strong accuracy--fairness trade-off under the proposed operational metric.
