Uncertainty-aware graph neural network for semi-supervised diversified recommendation
作者：
Minjie Cao*;Thomas Tran
通讯作者：
Minjie Cao
作者机构：
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
通讯机构：
School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, Canada
语种：
英文
关键词：
Recommendation systems,Uncertainty-aware pseudo-label selection,Diversity recommendation
期刊：
Social Network Analysis and Mining
ISSN：
1869-5450
年：
2024
卷：
14
期：
1
摘要：
Graphs are a powerful tool for representing structured and relational data in various domains, including social networks, knowledge graphs, and molecular structures. Semi-supervised learning on graphs has emerged as a promising approach to address real-world challenges and applications. In this paper, we propose an uncertainty-aware pseudo-label selection framework for promoting diversity learning in recommendation systems. Our approach harnesses the power of semi-supervised graph neural networks, utilizing both labeled and unlabeled data, to address data sparsity issues often encountered in real-world recommendation scenarios. Pseudo-labeling, a prevalent semi-supervised...
