Density-Based User Representation Using Gaussian Process Regression for Multi-Interest Personalized Retrieval
Abstract
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.
Cite
Text
Wu et al. "Density-Based User Representation Using Gaussian Process Regression for Multi-Interest Personalized Retrieval." Neural Information Processing Systems, 2024. doi:10.52202/079017-1666Markdown
[Wu et al. "Density-Based User Representation Using Gaussian Process Regression for Multi-Interest Personalized Retrieval." Neural Information Processing Systems, 2024.](https://mlanthology.org/neurips/2024/wu2024neurips-densitybased/) doi:10.52202/079017-1666BibTeX
@inproceedings{wu2024neurips-densitybased,
title = {{Density-Based User Representation Using Gaussian Process Regression for Multi-Interest Personalized Retrieval}},
author = {Wu, Haolun and Meshi, Ofer and Zoghi, Masrour and Diaz, Fernando and Liu, Xue and Boutilier, Craig and Karimzadehgan, Maryam},
booktitle = {Neural Information Processing Systems},
year = {2024},
doi = {10.52202/079017-1666},
url = {https://mlanthology.org/neurips/2024/wu2024neurips-densitybased/}
}