QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications

Abstract

IoT Mashup applications allow developer to compose existing Web APIs to create value-added composite Web services. The rapid growth of large-scale and complex services makes it difficult to find suitable Web APIs to build IoT Mashup applications for developers. Even if the existing service recommendation methods show improvements in service discovery, the accuracy of them can be significantly improved due to overlooking the impact of sparsity and multiple-dimension information of QoS between Mashup and services on recommendation accuracy. In this paper, we propose a QoS-aware service recommendation based on relational topic model and factorization machines for IoT Mashup applications. This method first uses relational topic model to characterize the relationships among Mashup, services, and their links, and mine the latent topics derived by the relationships. Second, it exploits factorization machines to train the latent topics for predicting the link relationship among Mashup and services to recommend adequate relevant top-k Web APIs for target IoT Mashup creation. Finally, we conduct a comprehensive evaluation to measure performance of our method. Compared with other existing recommendation approaches, experimental results show that our approach achieves a significant improvement in terms of precision, recall, and F-measure.