Journal
JOURNAL OF SUPERCOMPUTING
Volume -, Issue -, Pages -Publisher
SPRINGER
DOI: 10.1007/s11227-023-05582-9
Keywords
QoS prediction; Adaptive embedding representation; Neural collaborative filtering; Web service recommendation; User-service invocation modeling
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This study proposes an adaptive embedding representation-based QoS prediction method for Web services recommendation. By analyzing the problems of inaccurate semantic representation and inadequate service invocation modeling in QoS prediction within the cloud, this method improves the accuracy of QoS prediction.
Over the last few years, abundant and diverse Web services have migrated to the cloud. However, the disparity of the cloud environment renders quality of service (QoS) prediction harder. Based on analyzing the problems of inaccurate semantic representation and inadequate service invocation modeling in QoS prediction within the cloud, we propose an adaptive embedding representation-based QoS prediction method (AERQP) for Web services recommendation. First, the optimal embedding dimension of an explicit feature is determined dynamically by a policy network. Next, the embedding representation is remapped based on linear transformations. Then, global feature interactions are learned through a deep network with multi-head external attention as the core to fully model service invocations and realize accurate QoS prediction. Last, the experiment results indicate that AERQP improves an average of 44.8% and 16.9% on mean absolute error and root mean square error, respectively, compared to baseline methods.
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