Correlation-Based Top-k Recommendation for Web Services

Hybrid recommendation–based quality of service prediction for sensor services

“…Therefore, Sensor Web Services (SWS) describe the procedure of implementing a Web Service in certain WSNs, where the centre node works as a service, which is able to provide a tool for data exchange with nodes. 9 Recently, a range of researches 1,2,4,25 on Web Service recommendation are increasing in popularity. These researches can be applied in the recommendation for SWS.…”

“…Few learning techniques have been adopted in the model-based approaches like clustering models, 29 neural networks 30 and latent semantic models. 31,32 Most of the existing researches on Web Services recommendation [1][2][3][4]9,33 considered only the relationship between Web Services based on neighbourhood to predict the QoS attributes, but they ignored the user's latent interest in Web Services. This causes the existing recommendation methods for services to fall below the expectations of user requirements, despite the services characterizing higher QoS values.…”

“…The mining of user interested services is mainly based on topic models, a type of unsupervised generative probabilistic modelling. Several models have been proposed; M Ahmed et al 34 proposed a novel hybrid approach to make recommendations by combining the approach in Xie et al 2 with the LDA topic model to obtain the user-service probability matrix. The advantage of this method is that it considers both the correlation between services and the content of services.…”

“…Given the rapid development of service computing in the recent years, the way of offering computing services has taken various dimensions such as infrastructure as a service (IaaS), 1 software as a service (SaaS) 1 and platform as a service (PaaS). 1 And, service computing attracts much study, [2][3][4][5][6][7][8] including researches on the environment of wireless sensor networks (WSNs). [9][10][11][12] WSNs are typically composed of energy-constrained sensor nodes and a higher performing sink node.…”

“…In general, a recommendation system can be defined as a system that actively recommends items to users depending on the specific application areas. 2 The recommendation system is an important tool for providing personalized products to consumers. In general, the success of service offering in services lies in the effectiveness of the recommendation system.…”