Abstract: Disclosed is a method for recommending a Web service based on a graph attention convolutional network and graph pooling. The method includes: constructing a service network graph based on a dual-tower model, and differentiating a focus service; providing a dual-level aware self-attention mechanism in a graph convolution process, controlling an amount of information aggregated from a source node to a target node in a cluster-aware self-attention mechanism, and controlling an amount of information received by the target node from the source node in a query-aware self-attention mechanism; then, further extracting service information according to a graph pooling method taking a heterogeneous node coarsening strategy as a core; and splicing a dynamic representation, a graph-level representation, and a target service of the focus service, learning a composite embedding representation, and predicting a probability of interaction between the target service and a user at a next moment in a prediction layer.

Abstract: Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.

Abstract: Disclosed is a cloud-native application programming interface (API) recommendation method fusing data augmentation and contrastive learning. Service information is included on the basis of a service information double-graph structure, and a mutual attention mechanism is designed to compute an importance degree of each layer of information. A data optimization method for sequence information based on functional similarity and a computation method for similarity between services based on two parts of information are provided; on this basis, data of a service invocation sequence is augmented with the idea of contrastive learning to form an augmented sequence pair; a computational contrastive loss function is combined with a pair-wise recommendation loss function to optimize an overall model, thereby improving the effect of a service recommendation model; and according to a feature embedding representation result of a service, pair-wise recommendation scores are computed to complete service recommendation.