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First, CTLSVD applies SVD to divide the rating matrix into the user matrix and item matrix.
Second, through extracting more refined factor vectors, CTLSVD further employs SVD to divide the user matrix and item matrix into two matrices, respectively.
According to DeVellis, 18 several processes are necessary to generate an item pool; for example, developing conceptual definitions of each specified domain, formulating operational definitions of the domains, identifying observable indicators of each domain, and constructing a blueprint of the item matrix.
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This will lead to the user-item matrix with the nature of sparsity; specifically, the user-item matrix will be with low-rank.
The first category is filling the user-item matrix by default or by prediction [3].
Filling the user-item matrix by default is inefficient and erases users' personalized information.
Filling the user-item matrix by prediction is to predict scores according to the nearest neighbors of users or items.
Then, MF [2, 3, 4] methods play an important role in model-based CF methods, which aim to learn latent factors on user-item matrix.
If both Sin u) and Sin(i) are null sets during the user-item matrix filling phase in the offline training phase, pred u,i) = null.
Unfortunately, CF may lead to the poor recommendation when user ratings on items are very sparse in comparison with the huge number of users and items in user-item matrix.
Based on the prediction node sequence, we fill the user-item matrix step by step to generate a recommendation list, which can reduce the impact of a node's predication deviation on its nearest neighbors to really improve the prediction accuracy.
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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