计算机与数字工程2026，Vol.54Issue(2)：518-523,535,7.DOI:10.3969/j.issn.1672-9722.2026.02.037
基于位置信息图注意力机制的QoS预测
QoS Prediction Based on Location Information and Graph Attention Mechanism
摘要
Abstract
With the rapid development of the internet,the prediction of Quality of Service(QoS)in Web services has re-ceived increasing attention from scholars and has become a hot research topic in the field of service computing.Better prediction of QoS values in web services is an important research goal in this field.This paper proposes a QoS prediction method called LGAM based on location information and graph attention mechanism.The method combines location information with the attention mecha-nism of graph neural networks,builds a graph neural network with location information,and uses the message passing mechanism of graph neural networks for information fusion.The updated feature embedding vectors are then subjected to high-order feature inter-action using a deep neural network to obtain the prediction result.Experimental results show that this method outperforms the cur-rent state-of-the-art methods on the public WS-Dream dataset,with significant improvements in both MAE and RMSE,demon-strating the effectiveness of LGAM for QoS prediction.关键词
QoS预测/图神经网络/注意力机制Key words
QoS prediction/graph neural network/attention mechanism分类
信息技术与安全科学引用本文复制引用
程厚敏,章一磊,张梦蝶,张广泽..基于位置信息图注意力机制的QoS预测[J].计算机与数字工程,2026,54(2):518-523,535,7.基金项目
国家自然科学基金项目(编号:61802003)资助. （编号:61802003）
