  Recent years have witnessed the rapid development of service-oriented
computing technologies. The boom of Web services increases software developers'
selection burden in developing new service-based systems such as mashups.
Timely recommending appropriate component services for developers to build new
mashups has become a fundamental problem in service-oriented software
engineering. Existing service recommendation approaches are mainly designed for
mashup development in the single-round scenario. It is hard for them to
effectively update recommendation results according to developers' requirements
and behaviours (e.g. instant service selection). To address this issue, the
authors propose a service bundle recommendation framework based on deep
learning, DLISR, which aims to capture the interactions among the target mashup
to build, selected (component) services, and the following service to
recommend. Moreover, an attention mechanism is employed in DLISR to weigh
selected services when recommending a candidate service. The authors also
design two separate models for learning interactions from the perspectives of
content and invocation history, respectively, and a hybrid model called HISR.
Experiments on a real-world dataset indicate that HISR can outperform several
state-of-the-art service recommendation methods to develop new mashups
iteratively.
