Responsive and intelligent service recommendation method based on deep learning in cloud service

Abstract

The rapid expansion of the cloud service market is inseparable from its widely acclaimed service model. The rapid increase in the number of cloud services has resulted in the phenomenon of service overload. Service recommendations based on services' function attributes are important because they can help users filter services with specific functions, such as the function of guessing hobbies on shopping websites and 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) is very different. Although the recommendation based on services' function attributes satisfies users' basic demands, it ignores the impact of the QoS on the user experience. To further improve users' satisfaction with service recommendations, researchers try to recommend services based on services' non-functional attributes. There is sparsity of the QoS matrix in the real world, which brings obstacles to service recommendation; hence, the prediction of the QoS becomes a solution to overcome this obstacle. Scholars have tried to use collaborative filtering (CF) methods and matrix factorization (MF) methods to predict the 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 predicting the QoS value be more difficult. To solve the aforementioned problems, this study mainly does the following work: 1) we organized 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 predictions and compared some QoS prediction methods, such as CF and MF. The results showed that the prediction results based on GRU-GAN are far superior to other prediction methods under the same data density. We aim to help the engineering community promote their findings, shape the technological revolution, improve multidisciplinary collaborations, and collectively create a better future.

Keywords: QoS prediction; deep learning; intelligent; service recommendation; services.