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An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond
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This paper aims at a better understanding of matrix factorization (MF), factorization machines (FM), and their combination with deep algorithms' application in recommendation systems. Specifically, this paper will focus on Singular Value Decomposition (SVD) and its derivations, e.g Funk-SVD, SVD++, etc. Step-by-step formula calculation and explainable pictures are displayed. What's more, we explain the DeepFM model in which FM is assisted by deep learning. Through numerical examples, we attempt to tie the theory to real-world problems.
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Cited by 2 Pith papers
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A 1-by-1 convolutional autoencoder with six-way softmax outputs jointly predicts interaction likelihood and conditional rating, supported by generalization bounds and mixed but mostly competitive RMSE and Recall resul...
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Statistics of Min-max Normalized Eigenvalues in Random Matrices
For Gaussian random matrices with a rank-one mean, the large-N distribution of min-max normalized eigenvalues and the truncated factorization error depend only on J1/J0, via explicit formulas (9), (10), (18), (19).
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