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Collaborative filtering based on nonnegative/binary matrix factorization [PDF]

Frontiers in Big Data
Collaborative filtering generates recommendations by exploiting user-item similarities based on rating data, which often contains numerous unrated items.
Yukino Terui, Yuka Inoue, Yohei Hamakawa, Kosuke Tatsumura, Kazue Kudo, Kazue Kudo   +5 more
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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
doaj   +1 more source

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, Liqiu Gong, Zhichao Han, Peng Zhao, Junchao Yu, Suhua Wang   +5 more
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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, M. N. S. Swamy, Zhang-Quan Wang, Wai Ho Mow   +3 more
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Non-negative Matrix Factorization for Dimensionality Reduction [PDF]

ITM Web of Conferences, 2022
—What matrix factorization methods do is reduce the dimensionality of the data without losing any important information. In this work, we present the Non-negative Matrix Factorization (NMF) method, focusing on its advantages concerning other methods of ...
Olaya Jbari, Otman Chakkor
doaj   +1 more source

A Review on Quadrant Interlocking Factorization: WZ andWH Factorization

Journal of Nigerian Society of Physical Sciences, 2023
Quadrant Interlocking Factorization (QIF), an alternative to LU factorization, is suitable for factorizing invertible matrix A such that det(A) , 0.
Dlal Bashir, Hailiza Kamarulhaili, Olayiwola Babarinsa   +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, Erik Skau, Derek Desantis, Boian Alexandrov   +3 more
doaj   +1 more source

DRaW: prediction of COVID-19 antivirals by deep learning—an objection on using matrix factorization

BMC Bioinformatics, 2023
Background Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved ...
S. Morteza Hashemi, Arash Zabihian, Mohsen Hooshmand, Sajjad Gharaghani   +3 more
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Co-sparse Non-negative Matrix Factorization

Frontiers in Neuroscience, 2022
Non-negative matrix factorization, which decomposes the input non-negative matrix into product of two non-negative matrices, has been widely used in the neuroimaging field due to its flexible interpretability with non-negativity property.
Fan Wu, Jiahui Cai, Canhong Wen, Haizhu Tan   +3 more
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Probabilistic Matrix Factorization Recommendation of Self-Attention Mechanism Convolutional Neural Networks With Item Auxiliary Information

IEEE Access, 2020
To solve the problem of data sparsity in recommendation systems, this paper proposes a probabilistic matrix factorization recommendation of self-attention mechanism convolutional neural networks with item auxiliary information.
Chenkun Zhang, Cheng Wang
doaj   +1 more source