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.

电子邮件平台需要根据用户的偏好生成个性化的邮件排序，本文提出了一种基于多个标准的推荐算法 MOSR，并通过实验证明 MOSR 在满足用户的多样化偏好方面表现更好，同时维持了不同样本之间的稳定排序。

动态邮件重新排序问题的静态算法平衡

Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem

Anomaly detection algorithms are typically applied to static, unchanging,
data features hand-crafted by the user. But how does a user systematically
craft good features for anomalies that have never been seen? Here we couple
deep learning with active learning -- in which an Oracle iteratively labels
small amounts of data selected algorithmically over a series of rounds -- to
automatically and dynamically improve the data features for efficient outlier
detection. This approach, AHUNT, shows excellent performance on MNIST, CIFAR10,
and Galaxy-DESI data, significantly outperforming both standard anomaly
detection and active learning algorithms with static feature spaces. Beyond
improved performance, AHUNT also allows the number of anomaly classes to grow
organically in response to Oracle's evaluations. Extensive ablation studies
explore the impact of Oracle question selection strategy and loss function on
performance. We illustrate how the dynamic anomaly class taxonomy represents
another step towards fully personalized rankings of different anomaly classes
that reflect a user's interests, allowing the algorithm to learn to ignore
statistically significant but uninteresting outliers (e.g., noise). This should
prove useful in the era of massive astronomical datasets serving diverse sets
of users who can only review a tiny subset of the incoming data.

AHUNT 是一种结合深度学习和主动学习的异常检测算法，它可以自动和动态地改进数据特征以提高异常检测性能，并通过动态异常类别分类方法向完全个性化的异常类别分类方法迈进，使得算法能够学会忽略统计上显著但无趣的异常值。

学习检测有趣的异常

Learning to Detect Interesting Anomalies

Based on the success of recommender systems in e-commerce, there is growing
interest in their use in matching markets (e.g., labor). While this holds
potential for improving market fluidity and fairness, we show in this paper
that naively applying existing recommender systems to matching markets is
sub-optimal. Considering the standard process where candidates apply and then
get evaluated by employers, we present a new recommendation framework to model
this interaction mechanism and propose efficient algorithms for computing
personalized rankings in this setting. We show that the optimal rankings need
to not only account for the potentially divergent preferences of candidates and
employers, but they also need to account for capacity constraints. This makes
conventional ranking systems that merely rank by some local score (e.g.,
one-sided or reciprocal relevance) highly sub-optimal -- not only for an
individual user, but also for societal goals (e.g., low unemployment). To
address this shortcoming, we propose the first method for jointly optimizing
the rankings for all candidates in the market to explicitly maximize social
welfare. In addition to the theoretical derivation, we evaluate the method both
on simulated environments and on data from a real-world
networking-recommendation system that we built and fielded at a large computer
science conference.

研究发现，将现有的推荐系统直接应用于匹配市场是次优的。因此，提出了一种新的推荐框架来建模这种交互机制，并提出了高效的算法来计算个性化排名，并首次提出了联合优化所有市场候选人排名的方法来明确最大化社会福利。