  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.
