Cross-View Graph Alignment for Mashup Recommendation

Abstract

As the adoption of Service-Oriented Computing continues to grow, the number of web services has increased significantly, which makes service recommendation become an essential tool to assist users in selecting suitable services. However, a single service cannot satisfy the complex requirements of users, which has led to the emergence of a new technique known as Mashup, which combines services as reusable components to create value-added service compositions. Along with mashup, mashup recommendation has also become an indispensable and important component of service platforms. On service platforms, there are many heterogeneous entities and complex relationships between them. We divide these interaction into three different views: Mashup-Invocation view, Service-Consumption view, and Mashup-Composition view. As user preferences and characteristics of services and mashups are distributed across different views, their cooperation is crucial for accurate mashup recommendation. Therefore, we propose Cross-view Graph Alignment (CGA), a framework that captures the collaborative associations dispersed across different views and enhances the representation learning of users and mashups. This the first study to jointly tackle structure- and representation-level collaboration on the service platforms for better mashup recommendation. Experiments on two real-world service datasets show that CGA outperforms state-of-the-art methods and can better improve the mashup recommendation.