基于位置信息图注意力机制的QoS预测OA

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.