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Animator Matt "Thelaserbearguy" Ley gives me a two-minute blast of sharp insight into how YouTube works, explaining that the recommendation algorithm has moved on from the days when video views were the key metric.
For instance, it will use the recommendation algorithm that Amazon uses on its Web site to suggest certain apps, so if someone shops for March Madness gear on Amazon.com, the Appstore would recommend basketball apps.
For instance, expert traveler or system admin could not change the recommendation algorithm settings anymore.
The reliability of the recommendation algorithm is analyzed through statistical user feedback.
Finally, ATRS provides a facility for admin of the system or expert traveler to modify the recommendation algorithm parameters.
The goal of the recommendation algorithm is to find similar items rated by a user and to predict a rating based on the ratings of those similar items.
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Fig. 5 Runtime (milliseconds) of the recommendation algorithms for the different 243 authors.
The recommendation algorithms selected for this work are established in the literature.
For five recommendations, we find that the recommendation algorithms we investigate considerably limit the discoverability and navigability.
The similarities these recommendations are based on, however, are directly taken from the similarities used in the recommendation algorithms.
As for the recommendation algorithms, we find that the recommendations generated by an interpolation weights and matrix factorization performed best overall.
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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