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Based on this framework, we proposed an approach for filtering out malicious feedbacks and a trust metric to evaluate the trustworthiness of service provider.
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In real life, helpful and malicious feedback are often mixed together to feed the model.
And malicious feedback ratings are detected by adopting cumulative sum method.
Following a similar methodology as the previous experiments, such malicious feedback was provided for four rounds.
Their reputation measure has three phases (i.e., feedback checking, feedback adjustment, and malicious feedback detection) to enhance the accuracy.
It could be used to address situations where malicious feedback has been received but subsequent helpful feedback is not available.
The model is also shown to be robust with respect to malicious feedback, quickly recovering based on helpful user feedback.
Moreover, the feedback model is robust with respect to malicious feedback, quickly self-correcting based on subsequent helpful feedback from users.
It can avoid malicious feedback, but does not consider the impact of SLA requirements and the environment and cannot customise prediction for users.
We observe that the system error starts out with around 4m and quickly increases to 14m as a result of the malicious feedback.
Such a behaviour is not typical but it provides a "worst case scenario" study of the system, followed by its ability to recover from incorrect or malicious feedback.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com