A Deep Neural Network With Multiplex Interactions for Cold-Start Service Recommendation

As service-oriented computing (SOC) technologies gradually mature, developing service-based systems (such as mashups) has become increasingly popular in recent years. Faced with the rapidly increasing number of Web services, recommending appropriate component services for developers on demand is a vital issue in the development of mashups. In particular, since a new mashup to develop contains no component services, it is a new "user" to a service recommender system. To address this new "user" cold-start problem, we propose a multiplex interaction-oriented service recommendation approach, named MISR, which incorporates three types of interactions between services and mashups into a deep neural network. In this article, we utilize the powerful representation learning abilities provided by deep learning to extract hidden structures and features from various types of interactions between mashups and services. Experiments conducted on a real-world dataset from ProgrammableWeb show that MISR outperforms several state-of-the-art approaches regarding commonly used evaluation metrics.