Hybrid recommendation–based quality of service prediction for sensor services

Wireless sensor networks are being the focus of several research application domains, and the concept of sensing-as-a-service is on the rise in wireless sensor networks. Large service repositories comprising more services and functionalities usually impose new challenges to users while identifying their preferred services and may incur higher costs. Thereby, service recommendation systems have become important and integral tools of service models to provide personalized products for consumers. However, many existing methods of sensor service recommendation focus only on service discovery. To this end, this article proposes a novel hybrid recommendation method, named new hybrid recommendation method. First, latent Dirichlet allocation model is used to compute the similarity of the latent topics of the services, and the user’s latent semantic themes are used to extract the potential interest services. Moreover, the relevance of neighbourhood services is considered, which can improve the accuracy of quality of service prediction. Experiments conducted on real datasets demonstrate that the proposed method is more accurate than the existing methods of service recommendation.