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As for the specific DTSQ items, satisfaction with current treatment (item 1), treatment understanding (item 6), treatment recommendation (item 7) and continuing treatment (item 8), were the most strongly associated with the SF-36 scales.
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To find out relevant recommendation items from an educational point of view, a top down perspective can be used to design recommendations, especially for formal learning scenarios.
The use of context and information extracted from social network to support recommendation items is not new.
Self efficacy about the performance of physical activity will be assessed by seven (exercise programme) or eight (exercise recommendation) items based upon the Social Cognitive Theory.
These subscales (and number of items used) were: 1) waiting times (three items); 2) physical environment (three items); 3) accessibility (two items); 4) provider clinical information (four items); 5) personal interaction (two items); and 6) recommendation (one item).
Currently, Mahout offers implementations for user-based recommendations, item-based based recommendations, and several variations of matrix factorization.
Once the user (u_i) gets the list of recommendations for all items, he chooses the highest recommended item for him.
Especially for the item recommendation problem, we should recommend items for a user from all the items.
Again, this recommendation expands item 9, but specifically mentions MRI as a highly valuable imaging method for the potential follow-up of axSpA.
As for the other disease entities, this last recommendation expands item 9 and also the preceding one by reiterating the importance of comorbidities, and axial and soft tissue manifestations of PsA in the course of making treatment decisions.
Most existing content-based recommendation systems, in which items are usually described with keywords, are designed to recommend items according to text contents.
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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