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Regularization methods penalize high values for model parameters in order to avoid overfitting effects. For example the optimization (minimization) problem for a matrix factorization model used in a recommender system typically contains a penalty term for the elements of the lower-rank matrices used to approximate the user-item matrix, e.g. via the Frobenius norm.

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