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2022
DOI: 10.1016/j.techfore.2021.121303
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Operationalizing the telemedicine platforms through the social network knowledge: An MCDM model based on the CIPFOHW operator

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Cited by 22 publications
(5 citation statements)
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References 39 publications
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“…Meanwhile, the effect correlation of endogenous components was assessed using Q 2 . In this study, all Q 2 values were greater than 0.000, indicating that the model has impact correlation and further confirming the stability of this study [64].…”
Section: Direct Effectsupporting
confidence: 84%
“…Meanwhile, the effect correlation of endogenous components was assessed using Q 2 . In this study, all Q 2 values were greater than 0.000, indicating that the model has impact correlation and further confirming the stability of this study [64].…”
Section: Direct Effectsupporting
confidence: 84%
“…Future research could use different data to examine both the antecedents and consequences of trust in both dimensions of trust. While the social network concept can measure the degree of trust between experts [91,92], the trust relationship between the telemedicine platform and the user influences the evaluation, and future research is required.…”
Section: Limitations and Future Directionsmentioning
confidence: 99%
“…Then algorithms such as TF-IDF are utilized to match user preferences with courses (Ghauth et al 2010 ), thus implementing personalized recommendations for courses (Zhang et al 2017 ); (2) collaborative filtering method (CF). The common practice is to model users based on association rules (Aher and Lobo 2013 ), KNN (Murad et al 2020 ), social networks (Chen et al 2022 ; Zhang et al 2022 ; Zeng et al 2022 ), etc., to depict user portraits (Jing and Tang 2017 ), and then identify users with the same preference based on distance or similarity calculation methods, thus completing course recommendations; (3) machine learning-based recommendations. In this category, researchers mainly use machine learning methods to extract preference information of users (Hu et al 2022 ) or courses (Xu and Zhou 2020 ), and employ Bayesian neural networks (Li et al 2020 ), RNN (Okubo et al 2017 ) to characterize the courses, and make course recommendation; (4) Hybrid-based recommendation.…”
Section: Introductionmentioning
confidence: 99%