Email platforms need to generate personalized rankings of emails that satisfy user preferences, which may vary over time. We approach this as a recommendation problem based on three criteria: closeness (how relevant the sender and topic are to the user), timeliness (how recent the email is), and conciseness (how brief the email is). We propose MOSR (Multi-Objective Stationary Recommender), a novel online algorithm that uses an adaptive control model to dynamically balance these criteria and adapt to preference changes. We evaluate MOSR on the Enron Email Dataset, a large collection of real emails, and compare it with other baselines. The results show that MOSR achieves better performance, especially under non-stationary preferences, where users value different criteria more or less over time. We also test MOSR's robustness on a smaller down-sampled dataset that exhibits high variance in email characteristics, and show that it maintains stable rankings across different samples. Our work offers novel insights into how to design email re-ranking systems that account for multiple objectives impacting user satisfaction.

翻译：电子邮件平台需针对用户偏好（可能随时间变化）生成个性化邮件排序。我们将其视为基于三项准则的推荐问题：亲近度（发件人及主题与用户的相关性）、时效性（邮件的接收时间）、简洁性（邮件的篇幅）。本文提出MOSR（多目标平稳推荐器）——一种基于自适应控制模型的在线算法，能够动态平衡上述准则并适应偏好变化。我们在包含大量真实邮件的Enron电子邮件数据集上对MOSR进行评估，并与多个基线方法进行比较。结果表明，MOSR在非平稳偏好场景下（即用户对不同准则的重视程度随时间波动）表现更优。此外，我们在一个体现邮件特征高方差的降采样小规模数据集上测试MOSR的鲁棒性，证明其在各样本间均能保持稳定的排序结果。本研究为设计兼顾影响用户满意度的多目标邮件重排序系统提供了新思路。