Predicting QoS Values via Multi-dimensional QoS Data for Web Service Recommendations

Fast deployment of mobile Internet makes Web services often consumed under a multi-dimensional spatiotemporal model, wherein a specific service client could keep active while its location is changing. Recommending Web services for such clients must be able to predict unknown QoS values with the target client's service requesting time and location taken into account, e.g., Performing the prediction via a set of measured multi-dimensional QoS data. Most QoS prediction methods focus on the QoS characteristics for one specific dimension, e.g., Time or location, and do not exploit the structural relationships among the multi-dimensional QoS data. This paper proposes an integrated QoS prediction approach which unifies the modeling of multi-dimensional QoS data via multi-linear-algebra based concepts of tensor and enables efficient service recommendation for Web service based mobile clients via tensor decomposition and reconstruction optimization algorithms. Comparative experimental evaluation results show that the proposed QoS prediction approach could result in much better accuracy in recommending Web services than several other representative ones.