Matrix factorization in recommender systems: algorithms, applications, and peculiar challenges
FO Isinkaye - IETE Journal of Research, 2023 - Taylor & Francis
IETE Journal of Research, 2023•Taylor & Francis
Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that
has been successfully exploited in various applications, but sometimes they fail to provide
accurate recommendations because they depend majorly on the rating matrix, which is
always scanty and of very high dimension. Matrix factorization (MF) algorithms are variants
of latent factor models, which are easy, fast, and efficient. This article reviews the related
research and advances in the application of matrix factorization techniques in recommender …
has been successfully exploited in various applications, but sometimes they fail to provide
accurate recommendations because they depend majorly on the rating matrix, which is
always scanty and of very high dimension. Matrix factorization (MF) algorithms are variants
of latent factor models, which are easy, fast, and efficient. This article reviews the related
research and advances in the application of matrix factorization techniques in recommender …
Traditional Collaborative filtering (CF) is one of the techniques of recommender systems that has been successfully exploited in various applications, but sometimes they fail to provide accurate recommendations because they depend majorly on the rating matrix, which is always scanty and of very high dimension. Matrix factorization (MF) algorithms are variants of latent factor models, which are easy, fast, and efficient. This article reviews the related research and advances in the application of matrix factorization techniques in recommender systems. Popular matrix factorization algorithms utilized in recommender systems were reviewed. The peculiar challenges of using matrix factorization in recommender systems were also enumerated and discussed with the goal of identifying the different problems solved with the use of matrix factorization techniques as applied in recommender systems.
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