Accurate modeling of the diverse and dynamic interests of users remains a significant challenge in the design of personalized recommender systems. Existing user modeling methods, like single-point and multi-point representations, have limitations w.r.t.\ accuracy, diversity, and adaptability. To overcome these deficiencies, we introduce density-based user representations (DURs), a novel method that leverages Gaussian process regression (GPR) for effective multi-interest recommendation and retrieval. Our approach, GPR4DUR, exploits DURs to capture user interest variability without manual tuning, incorporates uncertainty-awareness, and scales well to large numbers of users. Experiments using real-world offline datasets confirm the adaptability and efficiency of GPR4DUR, while online experiments with simulated users demonstrate its ability to address the exploration-exploitation trade-off by effectively utilizing model uncertainty.
@article{arxiv.2310.20091,
title = {Density-based User Representation using Gaussian Process Regression for Multi-interest Personalized Retrieval},
author = {Haolun Wu and Ofer Meshi and Masrour Zoghi and Fernando Diaz and Xue Liu and Craig Boutilier and Maryam Karimzadehgan},
journal= {arXiv preprint arXiv:2310.20091},
year = {2024}
}