Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Abstract

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.

Research on the application of bilateral collaborative algorithm in hotel tourism consumption preference recommendation

“…19 For the complex project data, the information differentiation effect is reflected by the information entropy. 20 The calculation expression of information entropy is as formula (3).…”

“…The processing flow of the G-S bilateral preference model for bilateral targets is shown in Figure 5. 36 In the G-S model, tourists' satisfaction intention is first analyzed, and their project goals are analyzed. The preference intention is given, but tourists' self-searching for preferred services will reduce service desire.…”

“…In the traditional similarity scoring, the complex scene of tourist project data is not considered, which will affect the accuracy of the final similarity 19 . For the complex project data, the information differentiation effect is reflected by the information entropy 20 . The calculation expression of information entropy is as formula ().…”

“…When the tourist demand is greater than the inventory $$$ m $$$, the tourist demand at the end of the sorting needs to be removed, and the removed tourists will be arranged in the next cycle. The calculation of satisfaction value is shown in formula () 36 $$$$ {G}_{ij}=\sum \limits_{j=1}^m{x}_{ij}{Q}_{ij}{S}_{jigf} $$$$ …”

“…$$$ {Q}_{ij} $$$ represents the matching value between tourists and hotels. The processing flow of the G‐S bilateral preference model for bilateral targets is shown in Figure 5 36 …”