Responsive and intelligent service recommendation method based on deep learning in cloud service - Yu, Lei; Duan, Yucong (Frontiers in Genetics, 2022)
The rapid expansion of Cloud Service Market is inseparable from its widely acclaimed service model. The rapid increase in the number of Cloud Service has resulted in the phenomenon of Service Overload. Recommendations based on the services’ function attributes can help users filter services with specific functions, such as the function of guess hobbies in Shopping Websites and the daily recommendation functions in the Listening App. Nowadays Cloud Service Market has a large number of services, which have similar functions, but the Quality of Service (QoS) are very different. Although the recommendation based on the services’ function attributes satisfies users' demands to service functional, it ignores the impact of QoS on the user experience. In order to further improve users’ satisfaction to Service Recommendation, researchers try to recommend services based on services’ non-functional attributes. However, the sparsity of QoS matrix in the real world, which brings obstacles to service recommendation, the prediction of QoS becomes a solution to overcome this obstacle. Scholars have tried to use Collaborative Filtering (CF) methods and Matrix Factorization (MF) methods to predict QoS, but these methods face two challenges. The first challenge is the sparsity of data, the sparsity makes it difficult for CF to accurately determine whether users are similar, and the gap between the hidden matrices obtained by MF decomposition is large; the second challenge is the cold start of recommendation, when new users (or services) participate in the recommendation, its historical record is vacant, making accurately predict the QoS value be more difficult. In order to solve the above problems, this paper mainly does the following work: (1) We organize the QoS matrix into a service call record, which contains user characteristic information and current QoS. (2) We proposed a QoS prediction method based on GRU-GAN. (3) We used the time series data for quality prediction, and compared some QoS prediction methods, such as CF and MF. The results show that the prediction results based on GRU-GAN are far superior to other prediction methods under the same data density.