A Deep Learning based Personalized QoE/QoS Correlation Model for Composite Services

Classical services computing tasks such as service design and service recommendation need to comprehensively consider objective Quality of Services (QoS) and subjective Quality of Experiences (QoE) of users. There are close relationships between QoS and QoE, and how to construct an accurate QoS/QoE correlation model has been a hot topic in academia for years. Particularly, it is a challenge to construct such a model for complex composite services that are composed of services and their corresponding providers from multiple domains. This is because the number of QoS parameters is huge while the number of QoE parameters is comparatively smaller, and consequently, to reasonably encode the imbalanced QoS and QoE parameters of composite services becomes challenging. In addition, different users have different concerns and personalized experiences on the same service, and the QoS/QoE correlation model should be personalized, too; however, traditional end-to-end models which simply use QoS as input and QoE as output ignore such personalized preferences of different users, thus the model accuracy is not high enough. Based on the transformer pre-trained language model, this paper mines users' fine-grained concerns and their sentiment polarity from comments. Then, personalized preferences of users are encoded with CNN, QoS of composite services are encoded with multi-layer Bi-LSTM, and the QoS/QoE correlation is established based on the attention mechanism. In the experiments, our model achieves the highest on the accuracy of sentiment polarity prediction of user concerns, and the QoS and QoE encoded by the proposed model can accurately express the differentiated preferences of different users in a concrete composite service scenario. Potential downstream applications of the proposed QoS/QoE correlation model are comprehensively discussed.