Mobile Service Recommendation via Combining Enhanced Hierarchical Dirichlet Process and Factorization Machines

Abstract

Recently, Mashup is becoming a promising software development method in the mobile service computing environment, which enables software developers to compose existing mobile services to create new or value-added composite RESTful web application. Due to the rapid increment of mobile services on the Internet, it is difficult to find the most suitable services for building user-desired Mashup application. In this paper, we integrate word embeddings enhanced hierarchical Dirichlet process and factorization machines to recommend mobile services to build high-quality Mashup application. This method, first of all, extends the description documents of Mashup applications and mobile services by using Word2vec tool and derives latent topics from the extended description documents of Mashup and mobile services by exploiting the hierarchical Dirichlet process. Secondly, the factorization machine is applied to train these latent topics to predict the probability of mobile services invoked by Mashup and recommend mobile services with high-quality for Mashup development. Finally, the performance of the proposed method is comprehensively evaluated. The experimental results indicate that compared with the existing recommendation methods, the proposed method has significant improvements in MAE and RMSE.