Deep Learning Framework for Online Interactive Service Recommendation in Iterative Mashup Development

Recent years have witnessed the rapid development of service-oriented computing technologies. The boom of Web services increases the selection burden of software developers in developing service-based systems (such as mashups). How to recommend suitable follow-up component services to develop new mashups has become a fundamental problem in service-oriented software engineering. Most of the existing service recommendation approaches are designed for mashup development in the single-round recommendation scenario. It is hard for them to update recommendation results in time according to developers' requirements and behaviors (e.g., instant service selection). To address this issue, we propose a deep-learning-based interactive service recommendation framework named DLISR, which aims to capture the interactions among the target mashup, selected services, and the next service to recommend. Moreover, an attention mechanism is employed in DLISR to weigh selected services when recommending the next service. We also design two separate models for learning interactions from the perspectives of content information and historical invocation information, respectively, as well as a hybrid model called HISR. Experiments on a real-world dataset indicate that HISR outperforms several state-of-the-art service recommendation methods in the online interactive scenario for developing new mashups iteratively.Comment: 15 pages, 6 figures, and 3 table

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

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