@inproceedings{liu2023stationary,
author = {Liu, Jiayi and Neville, Jennifer},
title = {Stationary Algorithmic Balancing For Dynamic Email Re-Ranking Problem},
booktitle = {2023 Knowledge Discovery and Data Mining},
year = {2023},
month = {August},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/stationary-algorithmic-balancing-for-dynamic-email-re-ranking-problem/},
}