Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model

• Published in: IEEE access Vol. 7; pp. 55721 - 55731
• Main Authors: Guo, Lantian, Mu, Dejun, Cai, Xiaoyan, Tian, Gang, Hao, Fei
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Collaboration; Computational modeling; Data models; Mathematical analysis; Model accuracy; Prediction models; Predictive models; QoS prediction; Quality of service; service collaboration; service recommendation; Service-oriented tensor; Solid modeling; tensor decomposition; Tensors
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2912505

Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user's neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.