A QoS Prediction Framework via Utility Maximization and Region-Aware Matrix Factorization

With the surge of Web services, users are more concerned about Quality-of-Service (QoS) information when choosing Web services with similar functionalities. Today, effectively and accurately predicting QoS values is a tough challenge. Typically, traditional methods only use the QoS values provided by users to predict the missing QoS values, ignoring the arbitrariness of some users in providing observed QoS values and failing to consider the existence of anomalous QoS values with contingencies caused by some unstable Web services. Taking into account the above, this article proposes HyLoReF-us, a new framework for QoS prediction. HyLoReF-us uses the user reputation to measure the trustworthiness of users and the service reputation to measure the stability of web services. First, considering the utility generated by the invocation between users and Web services, HyLoReF-us employs a Logit model to calculate the user reputation and service reputation. Second, after combining the location information of users and services, as well as their reputations, HyLoReF-us obtains QoS predictions through an improved Matrix Factorization (MF) model. Finally, a series of experiments were conducted on the standard WS-DREAM dataset. Experimental results show that HyLoReF-us outperforms current state-of-the-art or baseline methods at Matrix Densities (MD) from 5% to 30%.