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

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. Howeve...

Full description

Saved in:
Bibliographic Details
Published inIEEE access Vol. 7; pp. 55721 - 55731
Main Authors Guo, Lantian, Mu, Dejun, Cai, Xiaoyan, Tian, Gang, Hao, Fei
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Online AccessGet full text

Cover

Loading…
More Information
Summary: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.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2912505