Computation of Recommender System Using Localized Regularization | Zendy

Kourosh Modarresi | Zendy

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Computation of Recommender System Using Localized Regularization

Author(s) -

Kourosh Modarresi

Publication year - 2015

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2015.05.422

Subject(s) - overfitting , computer science , regularization (linguistics) , missing data , computation , recommender system , algorithm , matrix (chemical analysis) , data mining , mathematical optimization , artificial intelligence , machine learning , mathematics , artificial neural network , materials science , composite material

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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