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On the one hand, the predictive accuracy metrics measure the error between the predicted rating and the real user rating.
Using this set, the final predicted rating is an average of the k most similar items' ratings, adjusted to their baseline estimates: hat{r}_{ui}=b_{ui}+frac{sum_{j in S^{k}(i u)} s_{ij}left(r_{uj}-b_{uj}right)}{sum_{j in S^{k}(i u)} s_{ij}}.
The predicted rating for i by u is then computed as the sum of the ratings of (j_1, dots j_k) weighted by their similarity to i.
Since it performs the rating prediction task and then through the ratings, it generates a ranking of the 100 items with the highest predicted rating, it is assumed that the final ranking contains items which are not in the test set, since it is a drawn portion of the 10% of the database.
Second, the items with the highest predicted rating among this set are recommended.
We use the deviation to adjust the predicted rating to gain better results.
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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