QoS prediction for smart service management and recommendation based on the location of mobile users

Quality of Service (QoS) directly reflects the degree to which services offered by providers satisfy the non-functional requirements of users. QoS information is not usually available as a priori to providers when recommending services to user queries, this creates uncertainty in offering right services to right queries. Recent researches in service recommendation and management mainly address the issues of sparse data prediction and user personalized recommendation. Recommendation systems require smart strategies of recommending and managing services in accordance with the user queries. Predicting the QoS requirements of user queries before recommending the services can potentially aid in offering the most suitable services to users. This paper proposes a hybrid mobile service recommendation and management model based on semantic recommendation along with location-based quality preference analysis for emerging 5G mobile networks. The proposed model can effectively predict the QoS by exploiting previously invoked services to identify the best matching mobile services based on the similarity between users and services. Performance evaluation based on a published web services dataset demonstrates an enhanced prediction accuracy with an effective reduction in time overheads when compared to other related methods.