Research on cloud manufacturing service recommendation based on graph neural network

Abstract

There are an increasing number of manufacturing service resources appeared on the cloud manufacturing (CMfg) service platform recently, which leads to a serious information overloading problem to the enterprises that need these resources. To tackle this problem, a graph neural network-based recommendation method for CMfg service resources is proposed, which effectively overcomes some limitations of the traditional recommendation methods. Specifically, we first use different similarity calculation methods (e.g., Cosine similarity, Pearson correlation coefficient, etc.) to calculate the similarities between different resources based on the feature information of CMfg service resources. A resource graph dataset is accordingly established. A graph neural network is then used to perform representation learning of nodes in these graphs, obtaining the vector representations of these nodes. Finally, new links that may appear in a graph are predicted by performing dot product calculations on these nodes’ vector representations. And these links can be used to recommend suitable resources. Experiments mainly show that (i) the proposed method obtains better link prediction accuracy compared with that of other link prediction algorithms; (ii) when the network density used for training is relatively high, the predictive performance of the proposed method is improved significantly. Our method can shed light on how to choose suitable CMfg service resources from the CMfg service platform.

Knowledge distillation representation and DCNMIX quality prediction‐based Web service recommendation

“…In order to obtain more structural subgraph information, they utilize a motif-based attention mechanism to mine high-order interaction information of various motifs and propose a Motif-based graph attention network service recommendation model. To solve the problem of service resource information overload, Li et al 23 propose a service resource recommendation method based on a graph neural network. This method first uses different similarity formulas to calculate the similarity of service resources and establishes the corresponding resource graph data set, and then utilizes the graph neural network learns the vector representation of nodes in the graph, and finally combines the link prediction algorithm to implement service recommendation.…”

“…AUC represents the area under the ROC curve. The higher the value of AUC, the better the effect of the recommended model, as shown in formula (23):…”