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.
Highlights
Service computing offers an exciting paradigm for service provision and consumption. e field is embracing new opportunities in the mobile Internet era, which is characterized by ubiquitous wireless connectivity and powerful smart devices that let users consume or provide services anytime and anywhere [1]
(iv) latent Dirichlet allocation (LDA)-FMs. e topic probability distributions of description documents in Mashup and mobile service firstly are derived by the LDA model, and they are trained via FMs to predict the probability distribution of mobile service invoked by Mashup and recommend mobile service with high quality
We select the optimal number of extended words for each original word in description documents of Mashup and service, to achieve the best recommendation result in our EHDP-FMs
Summary
Service computing offers an exciting paradigm for service provision and consumption. e field is embracing new opportunities in the mobile Internet era, which is characterized by ubiquitous wireless connectivity and powerful smart devices that let users consume or provide services anytime and anywhere [1]. E field is embracing new opportunities in the mobile Internet era, which is characterized by ubiquitous wireless connectivity and powerful smart devices that let users consume or provide services anytime and anywhere [1]. As emerging techniques such as cloud and mobile computing become more prevalent, the way we provide and consume services is everchanging [1]. Due to the rapid development and effective utilization of mobile computing technologies, services are no longer limited to traditional platforms and contexts. Mashup technology is becoming a popular software development method in the mobile service computing environment. To solve the above challenges, some researchers use service recommendation
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