Computation of Recommender System Using Localized Regularization

Online and offline targeting and recommendations are major topics in ecommerce. The topic is treated as Matrix Completion, Missing Values and Matrix Imputations in statistics. The main goal in all of these fields is to compute the unknown (missing) values in the data matrix. In computing or recovering the unknown entries of the matrix, overfitting may happen which is due to the lack of sufficient information and thus some penalization of the objective function in the form of regularization becomes necessary. This work is based on a different view of regularization, i.e., a localized regularization technique which leads to improvement in the estimation of the missing values.

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