Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem
This work addresses personalized email ranking for users, but it is incremental as it builds on existing recommendation methods with a focus on multi-objective balancing.
The paper tackled the dynamic email re-ranking problem by proposing MOSR, an online algorithm that adaptively balances relevance, timeliness, and conciseness criteria, achieving better performance, especially under non-stationary preferences, as shown on the Enron Email Dataset.
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.