Hierarchical Hypergraph-based Attention Neural Network for Service Recommendation

Abstract

With the rapid growth of various services and APIs on the Internet and the Web,it has become increasingly challenging for developers to quickly and accurately find APIs that meet their needs,thus requiring an efficient recommendation system.Currently,the application of graph neural networks in service recommendation has achieved great success,but many such methods are still limited to simple interactions and ignore the intrinsic relationships between mashups and API calls.To address this issue,this paper proposes a hierarchical hypergraph-based attention neural network for service recommendation method(H-HGSR) for API recommendation.First,eight types of hyperedges are defined,and the corresponding hypergraph adjacency matrix generation methods are explored.Then,node-level and hyperedge-level attention mechanisms are proposed.The node-level attention mechanism is used to aggregate important information from different neighbors under specific types of hypergraph adjacency matrices to capture high-order relationships between mashups and APIs.The hyperedge-level attention mechanism is used to weight the combination of node embeddings generated from different types of hypergraph adjacency matrices.By learning the importance of node-level and hyperedge-level attention,more accurate embedding representations can be obtained.Finally,a multi-layer perceptron neural network(MLP) is used for service recommendation.Extensive experiments are conducted on the Programmable Web real dataset,and the overall comparison results show that the proposed H-HGSR framework outperforms the state-of-the-art service recommendation methods.