Light Heterogeneous Hypergraph Contrastive Learning Based Service Recommendation for Mashup Creation

Mashup technology enables developers to create new applications more readily by combining existing services. As its popularity grows, research on service recommendation for mashup creation has gained increasing attention. Existing recommendation methods have the following limitations: either they are susceptible to data sparsity problems, or they exhibit over-smoothing when aggregating high-order neighbors, resulting in similar and non-specific node feature representations, or they only focus on bipartite graphs and neglect the rich heterogeneous information in the mashup-service ecosystem. To address these issues, we propose a service recommendation method for mashup creation based on l ight h eterogeneous hyper g raph c ontrastive l earning (LHGCL). This method first constructs a heterogeneous hypergraph by combining mashup information, service information, the mashup-service interaction data, and their related attribute information. Then, it designs a light hypergraph neural network to capture the high-order relationships between mashups and services. Next, it applies contrastive learning to enhance the representations of mashups and services. Finally, it utilizes the enhanced feature vectors of mashups and services to predict mashup preferences for services. Comprehensive experiments conducted on the real-world ProgrammableWeb dataset demonstrate the superiority of the proposed method and the effectiveness of its key modules.