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This means that this MBC activity didn't involve in-depth learning processes such as proposing a recommendation with justification, outlining the advantages and disadvantages of a therapy regimen, and making clinical judgments supported by evidence.
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Zheng et al. proposed a recommendation system that recommends activities using GPS histories and also recommends locations using user activities [28].
Dovgopol et al. propose a recommendation model based on k-Nearest Neighbor and Naive Bayes [26].
We propose a recommendation procedure for online book communities called "Commenders".
Boukhris et al. [17] proposed a recommendation technique for academic venues for a particular target researcher, TR.
Wang and Chen [15] proposed a recommendation system that uses reviews to obtain characteristics and their sentiment for the construction of profiles.
Our work proposes a recommendation service that suggests frequent combinations of tasks, thus accelerating the workflow design.
Also for scientific workflows, [33] proposes a recommendation service that uses sequence mining, a data mining technique for finding frequent sequential events in a dataset.
Sheng Wang et al. [13] proposed a recommendation algorithm based on Bayesian personalized ranking (BPR) by modeling user's implicit feedbacks in micro-blogging services.
To this end, in this paper, we propose a recommendation in feature space sphere (RFSS) which takes into account the relationship between users and items in feature space.
The aim of this paper is to propose a recommendation system that considers user's functionality levels regarding physical or psychological limitations.
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