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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https://doi.org/10.1007/978-3-030-59592-0artificial intelligence; bandwidth; computer networks; computer systems; databases; Human-Computer Intera

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978-3-030-59591-3Springer Nature Switzerland AG 2020

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Services Computing – SCC 2020978-3-030-59592-0Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Web Service Composition by Optimizing Composition-Segment Candidatesbuilding a web service dependency graph. Experimental results on both Web Service Challenge 2009’s datasets and substantial datasets randomly generated show that the proposed method outperforms the state-of-art while achieving a much ideal tradeoff among all the objectives with better performance.

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Collaborative Learning Using LSTM-RNN for Personalized Recommendation sequence-to-sequence recommendations. Our proposed model builds on the strength of collaborative filtering while preserving individual user preferences for personalized recommendation. We conduct experiments using Movielens (.) dataset to evaluate our proposed model and empirically demonstrate that

HAUNT

An Attention Model for Mashup Tag Recommendationsubsequently recommend top-N words with highest attention weights as tags. Our model is based on the intuition that not every word in a mashup description is equally relevant in identifying its functional aspects. Therefore, determining the relevant sections involves modeling the interactions of the

一窝小鸟

On the Diffusion and Impact of Code Smells in Web Applicationss to monitor the existence and spread of such anti-patterns. In this paper, we specifically target web apps built with PHP being the most used server-side programming language. We conduct the first empirical study to investigate the diffuseness of code smells in Web apps and their relationship with