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

adduction

Book 2016ts well-known decomposition methods for recommender systems, such as Singular Value Decomposition (SVD), UV-decomposition, Non-negative Matrix Factorization (NMF), etc. and describes in detail the pros and cons of each method for matrices and tensors. This book provides a detailed theoretical mathem

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Related Work on Tensor Factorizationzed is the low-order tensor decomposition (LOTD) method. This method has low functional complexity, is uniquely capable of enhancing statistics, and avoids overfitting compared with traditional tensor decompositions such as TD and PARAFAC.

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https://doi.org/10.1007/978-3-319-41357-0Recommender Systems; Information Retrieval; Factorization Methods; Machine Learning; Matrix Factorizatio

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Matrix and Tensor Factorization Techniques for Recommender Systems978-3-319-41357-0Series ISSN 2191-5768 Series E-ISSN 2191-5776

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Conclusions and Future WorkIn this chapter, we will discuss the main conclusions of the experimental evaluation and the limitations of each algorithm, and will provide the future research directions.

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Multiple Vector Seeds for Protein Alignmenttion of . [3] to reduce noise hits. We model picking a set of vector seeds as an integer programming problem, and give algorithms to choose such a set of seeds. A good set of vector seeds we have chosen allows four times fewer false positive hits, while preserving essentially identical sensitivity a