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The ubiquity of the Internet of Things (IoT) across diverse applications underscore its pivotal role in seamlessly integrating physical devices to facilitate efficient data collection, analysis, and automation. Consequently, ensuring the Quality ...

The proliferation of Web APIs has facilitated the creation of numerous software applications through the integration of diverse services, commonly referred to as mashups. However, the growing complexity and number of available Web APIs pose ...$\mathrm{}$

Recommending a certain number of services based on QoS (Quality of Service) is a very widespread problem. Traditional approaches use the QoS advertised by service providers to calculate either the top-k services or the skyline ones. The drawback ...

Service discovery and recommendation has been considered an effective technique for freeing workflow developers out from time-consuming work of manually selecting suitable software services from a sea of service candidates. Facing complex ...

Choosing appropriate Web services to create new applications plays a significant role for service-based development. The general service recommendation approaches usually prefer to suggest popular and frequently used services. However, nowadays ...

• A long tail service recommendation method is proposed for application creation.
• Contrastive learning is used globally and locally to capture implicit connection.
• Double views are used to mitigate the problem of data sparsity and ...

Service recommendation is an efficient method for service-oriented software that can improve software quality. Applications often require the integration of multiple services to create more powerful and complex functionality while saving software ...

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• A serivce recommendation model is given for application creation.
• The service recommendation applies contrastive learning and multi-task learning.
• The service recommendation enables a joint modeling of various information.
• ...

In recent years, service-oriented computing technology has developed rapidly, which, however, has increased the burden of selection for software developers when developing service-based systems. To solve this problem, people have proposed various ...

• An iterative service recommendation method is proposed for application creation.
• Two separate models are used to capture the application preference.
• Multi-task learning is used to achieve the effect of implicit data enhancement.

With the rapid progress of Mobile Internet of Things (MIoT), Location-Based Services (LBSs) have become widely popular due to the deployment of diverse devices and facilities. The utilization of spatial–temporal information has led to a ...

A service recommendation system is an information system that helps build mashups quickly to implement new features in response to environmental changes. With the development of deep learning (DL) in recent years, more researchers have started ...

Deep Neural Networks (DNN) based collaborative filtering has been successful in recommending services by effectively generalizing graph-structured data. However, most existing approaches focus on first-order interactions. Although recent ...

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• We report a Motif-based Graph Attention Network for service recommendation (MGSR).
• The model aggregates high-order information of motif-based neighbors.
• The model identifies all the seven up-to-four-node motifs in bipartite graphs.

The inability to choose an excellent service recommended by a system simply because it is not available is common when people use service recommendation systems. Traditional research on recommendation systems has focused on the user profile, QoS(...

In recent years, the world has witnessed the increased maturity of service-oriented computing. The mashup, as one of the typical service-based applications, aggregates contents from more than one source into a single user interface. Facing the ...

With the rapid development of service computing technology, more and more companies and organizations are encapsulating and publishing their operational data or resources to the Internet in the form of Web services, resulting in an exponential ...$\mathit{}$$\mathit{}$

In the era of network information overload, personalized service recommendation is paid more and more attention by researchers, and Quality of Service (QoS) is a key criterion for service selection and recommendation. QoS is described as a non-...

The era of everythingasaservice led to an explosion of services with similar functionalities on the internet. Quickly obtaining a high-quality service has become a research focus in the field of service recommendation. Studies show that quality of ...

• To solve the high data sparsity and low recommendation accuracy in the traditional service recommendation models under mobile cloud, the work proposed hybrid ...

The rapid development of the web has led to a considerable increase in information dissemination. Recently, personalized web service recommendation has become a popular research area in service computing. Research on web service ...

During the recent years, several new Information and Communication Technology solutions have been developed in order to meet the increasing needs of elderly with cognitive impairments and support their autonomous living. Most of these solutions ...

The rise of high-quality cloud services has made service recommendation a crucial research question. Quality of Service (QoS) is widely adopted to characterize the performance of services invoked by users. For this purpose, the QoS prediction of ...

When exploring high-order neighbors for embedding learning, data sparsity problems in service recommendation system can be compensated via Graph Convolutional Network (GCN). However, the performance of GCN will deteriorate when stacking more ...