Web service discovery based on maximum weighted bipartite graphs

Kuo, Chang‐Fu; Kuang, Chunxiang

doi:10.1016/j.comcom.2021.01.031

Cited by 8 publications

(1 citation statement)

References 10 publications

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“…As a result, web services have a prominent role in software development and software engineering. 1 Nowadays, a large number of studies on different web service related challenges such as service discovery, [2][3][4] service composition, [5][6][7][8] service ranking, [9][10][11] and service recommendation [12][13][14] have been done. In all the above challenges, selecting the appropriate web services among the huge set of candidate web services is important.…”

Section: Introductionmentioning

confidence: 99%

A new model for trustworthy web service QoS prediction

Hashemi

Ghafouri

Hung

2021

Concurrency and Computation

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The number of web services available on the internet has exploded, and as a result, the number of services with the same functionality has exploded as well. Therefore, selecting the best web service from functionally similar services is a critical task in the web service domain. The Quality of Service (QoS) is one of the most common criteria used to select the best web service. Collaborative filtering (CF) has been utilized in several studies to predict the values of QoS attributes of web services for each user in a personalized way. The QoS histories of other users are employed in these methods to predict the QoS values of the active user. Although these methods function well and produce acceptable prediction results, the accuracy of their predictions can be harmed by incorrect data provided by untrustworthy users. In this study, we propose a new model that reduces the impact of unreliable user data, resulting in a trustworthy prediction. This model can be applied to any existing prediction method. In experiments, the proposed model was applied to seven known prediction methods. The results indicate that this model is able to eliminate the impact of unreliable users.

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