Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem

TL;DR

This paper introduces MOSR, an adaptive online algorithm for dynamic email re-ranking that balances relevance, recency, and brevity to improve user satisfaction amid changing preferences.

Contribution

The paper presents MOSR, a novel multi-objective online algorithm that dynamically balances email ranking criteria and adapts to evolving user preferences.

Findings

MOSR outperforms baseline methods on the Enron dataset.

MOSR maintains stable rankings across high-variance datasets.

The approach effectively adapts to non-stationary user preferences.

Abstract

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.