Forecasting Time-aware Nonfunctional Service Attributes Using Deep Learning

Service Oriented Architecture (SOA), as a new architecture paradigm, has been dramatically developed. A growing number of Web services have been developed and published on the Web, which increases the burden of service selection in system construction. Nonfunctional attributes of services have an essential impact on service selection. Users may observe the dynamic nonfunctional property values of different web services in various locations due to the dynamic attributes of various web services. How to accurately predict user perception of nonfunctional attributes of services has become an important problem in services computing. Towards this issue, this paper improves the personalized LSTM model by adding the attention mechanism to obtain more accurate vectorization representations of users and services, so as to better forecast missing nonfunctional service attributes. The proposed approach is evaluated on a real-world dataset, and experiment results show its feasibility.