Service Recommendation in an Evolving Ecosystem: A Link Prediction Approach

Services computing is playing a critical role in recent years in many fields and we observe a rapidly increasing number of web accessible services and their compositions nowadays. However, our earlier empirical study reveals that, overall the public available services are under-utilized, and when they are used, they are used mostly in an isolated manner. This phenomenon inspires us to further explore a methodology to help consumers understand the usage pattern of the service ecosystem, including interactions among services, and the evolution of these interactions. Based on the derived usage pattern, this methodology also introduces a service recommendation method that suggests both services and their compositions, in a time-sensitive manner. We firstly construct an evolution network model from the historical usage of the services in the ecosystem. Then a rank-aggregation-based link prediction method is proposed to predict the evolution of the ecosystem. Based on this link prediction method, we can recommend services and compositions of interest to service developers. Through an experiment on the real-world mashup-service ecosystem, i.e., Programmable Web, we demonstrated that our approach can effectively recommend services and compositions with better precision than the methods we compared.