Titlebook: Services Computing – SCC 2020; 17th International C Qingyang Wang,Yunni Xia,Liang-Jie Zhang Conference proceedings 2020 Springer Nature Swi

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QoS Time Series Modeling and Forecasting for Web Services: A Comprehensive Survey of Subsequent Appltudies and approaches have been produced. However, these existing efforts lack a comprehensive and detailed review that profoundly and systematically organizes, analyzes, and discusses this body of work. Thus, to fill this gap, the authors offered the paper ., in which four essential research concer

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Web Service Composition by Optimizing Composition-Segment Candidatess. Despite many researches have already addressed the optimization of multiple Quality of Service (QoS) attributes, most of the currently available methods have to build a large web service dependency graph, which may incur excessive memory consumption and extreme inefficiency. To address these issu

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Collaborative Learning Using LSTM-RNN for Personalized Recommendation, despite the myriad of available online activity information, most existing time-based recommender systems either focus on predicting some user rating, or rely on information from similar users. These systems, therefore, disregard the temporal and contextual aspects of users preferences, revealed i

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An Attention Model for Mashup Tag Recommendationhe number of available mashups over the internet. The increase however, poses a new requirement of organizing and managing these mashups for better understanding and discovery. For this reason, tags have become highly important because they describe items and allows for easy discovery. Most existing

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