Service Recommendation for Mashup Creation Based on Time-Aware Collaborative Domain Regression

Abstract

Mash up has emerged as a promising way to compose web APIs and create value-added compositions. The increasing of APIs demands more accurate recommendation algorithms. However, service domain evolution, mash up-side cold-start and information evaporation are somehow overlooked by existing work. In this paper, by extending the collaborative topic regression (CTR) model, the procedure of service selection is modeled with a generative process, and the mash up-side cold-start problem that cannot be dealt with by naïve CTR is resolved. By learning the maximum a posteriori estimates of the whole generative process, both content information and historical usage are taken into consideration to extract service domains, thus the service domains can evolve with the evaluation of historical usage pattern. Meanwhile, information evaporation is also considered by giving time-related confidence levels to historical usage to track the evolution of service ecosystem. Experiments on the real-world Programmable Web data set show that compared with the state-of-the-art methods, our approach gains a 6.8% improvement in terms of recommendation accuracy.