Personalized Web Service Recommendation Based on QoS Prediction and Hierarchical Tensor Decomposition

IEEE Access

Jun Zeng¹  Qingyu Xiong¹  Tian Cheng¹  Wei Zhou¹  Junhao Wen¹  Xueyuan Cai² 

[1] School of Big Data and Software Engineering, Chongqing University, Chongqing, China;Shenzhen Vispractice Technology Corporation, Shenzhen, China;

关键词: QoS;    service computing;    tensor decomposition;   

DOI  :  10.1109/ACCESS.2019.2909548

来源: DOAJ

【 摘 要 】

Web service recommendation based on the quality of service (QoS) is important for users to find the exact Web service among many functionally similar Web services. Although service recommendations have been recently studied, the performance of the existing ones is unsatisfactory because: 1) the current QoS predicting algorithms still experience data sparsity and cannot predict the QoS values accurately and 2) the previous approaches fail to consider the QoS variance according to the users and services' locations carefully. A Web service recommendation method based on the QoS prediction and hierarchical tensor decomposition is proposed in this paper. The method is called QoSHTD that is based on location clustering and hierarchical tensor decomposition. First, the users and services of the QoSHTD cluster into several local groups based on their location and models local and global triadic tensors for the user-service-time relationship. The hierarchical tensor decomposition is then performed on the local and global triadic tensors. Finally, the predicted QoS value through local and global tensor decomposition is combined as the missing QoS values. The comprehensive experiment shows that the proposed method achieves a high prediction accuracy and recommending quality of Web service, and can partially address data sparsity.

【 授权许可】

Unknown