20 research outputs found
Density-based User Representation through Gaussian Process Regression for Multi-interest Personalized Retrieval
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, computational
cost, and adaptability. To overcome these deficiencies, we introduce
density-based user representations (DURs), a novel model that leverages
Gaussian process regression 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.Comment: 16 pages, 5 figure