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Fairer non-negative matrix factorization. [PDF]
Front Big DataThere has been a recent critical need to study fairness and bias in machine learning (ML) algorithms. Since there is clearly no one-size-fits-all solution to fairness, ML methods should be developed alongside bias mitigation strategies that are practical
Kassab L +5 more
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Collaborative filtering based on nonnegative/binary matrix factorization. [PDF]
Front Big DataCollaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Terui Y +4 more
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Deep maximum margin matrix factorization. [PDF]
Sci RepCollaborative filtering (CF) over ordinal feedback is naturally organized as a problem of matrix completion, where the input consists of a partially observed user-item interaction matrix. Maximum Margin Matrix Factorization (MMMF) has achieved widespread
Kumar S, Kagita VR, Kumar V, Niranjan G.
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Some properties of various types of matrix factorization [PDF]
ITM Web of Conferences, 2021 Matrix factorizations or matrix decompositions are methods that represent a matrix as a product of two or more matrices. There are various types of matrix factorizations such as LU factorization, Cholesky factorization, singular value decomposition etc ...
Ng Wei Shean, Tan Wei Wen
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Neural Metric Factorization for Recommendation
Mathematics, 2022 All current recommendation algorithms, when modeling user–item interactions, basically use dot product. This dot product calculation is derived from matrix factorization.
Xiaoxin Sun +5 more
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Electronic Journal of Statistics, 2023 We investigate a general matrix factorization for deviance-based data losses, extending the ubiquitous singular value decomposition beyond squared error loss. While similar approaches have been explored before, our method leverages classical statistical methodology from generalized linear models (GLMs) and provides an efficient algorithm that is ...
Liang Wang, Luis Carvalho
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Matrix Factorization Techniques in Machine Learning, Signal Processing, and Statistics
Mathematics, 2023 Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or compressible signals. Sparse coding represents a signal as a sparse linear combination of atoms, which are elementary signals derived from a predefined dictionary ...
Ke-Lin Du +3 more
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MatMat: Matrix Factorization by Matrix Fitting [PDF]
2021 IEEE 4th International Conference on Information Systems and Computer Aided Education (ICISCAE), 2021 Matrix factorization is a widely adopted recommender system technique that fits scalar rating values by dot products of user feature vectors and item feature vectors. However, the formulation of matrix factorization as a scalar fitting problem is not friendly to side information incorporation or multi-task learning. In this paper, we replace the scalar
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Localization of Matrix Factorizations [PDF]
Foundations of Computational Mathematics, 2014 Matrices with off-diagonal decay appear in a variety of fields in mathematics and in numerous applications, such as signal processing, statistics, communications engineering, condensed matter physics, and quantum chemistry. Numerical algorithms dealing with such matrices often take advantage (implicitly or explicitly) of the empirical observation that ...
Ilya A. Krishtal +2 more
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Boolean Matrix Factorization via Nonnegative Auxiliary Optimization
IEEE Access, 2021 A novel approach to Boolean matrix factorization (BMF) is presented. Instead of solving the BMF problem directly, this approach solves a nonnegative optimization problem with an additional constraint over an auxiliary matrix whose Boolean structure is ...
Duc P. Truong +3 more
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