Titlebook: Matrix and Tensor Factorization Techniques for Recommender Systems; Panagiotis Symeonidis,Andreas Zioupos Book 2016 The Editor(s) (if appl

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2191-5768 blend of theory and practice, making it suitable for students, researchers and practitioners interested in both recommenders and factorization methods. Lecturers can also use it for classes on data mining, reco978-3-319-41356-3978-3-319-41357-0Series ISSN 2191-5768 Series E-ISSN 2191-5776

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ies eine vollständige Aufzählung sein könnte. Überhaupt ist die Entstehung der Theorie der Splines ein Beispiel für eine Entwicklung, die durch praktische Erfordernisse ins Leben gerufen wurde. Diese praktischen Erfordernisse bestanden damals in der Notwendigkeit, über anwendbare Methoden zur glatte

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Matrix and Tensor Factorization Techniques for Recommender Systems

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Introduction, and information retrieval. Recommender systems deal with challenging issues such as scalability, noise, and sparsity and thus, matrix and tensor factorization techniques appear as an interesting tool to be exploited. That is, we can deal with all aforementioned challenges by applying matrix and te

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Related Work on Matrix Factorizationion, which decomposes the initial matrix into a canonical form. The second method is nonnegative matrix factorization (NMF), which factorizes the initial matrix into two smaller matrices with the constraint that each element of the factorized matrices should be nonnegative. The third method is laten

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Performing SVD on Matrices and Its Extensionsal background and present (step by step) the SVD method using a toy example of a recommender system. We also describe in detail UV decomposition. This method is an instance of SVD, as we mathematically prove. We minimize an objective function, which captures the error between the predicted and real

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Experimental Evaluation on Matrix Decomposition Methodsalgorithm combined with SVD. For the UV decomposition method, we will present the appropriate tuning of parameters of its objective function to have an idea of how we can get optimized values of its parameters. We will also answer the question if these values are generally accepted or they should be

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Related Work on Tensor Factorizationrst method that is discussed is the Tucker Decomposition (TD) method, which is the underlying tensor factorization model of Higher Order Singular Value Decomposition. TD decomposes a tensor into a set of matrices and one small core tensor. The second one is the PARAFAC method (PARAllel FACtor analys