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An Introduction to Matrix factorization and Factorization Machines in Recommendation System, and Beyond

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arxiv 2203.11026 v1 pith:B7LFMFAN submitted 2022-03-12 cs.IR cs.LG

Yuefeng Zhang This is my paper · ORCID
classification cs.IRcs.LG
keywords factorizationdeepmachinesmatrixrecommendationaimsalgorithmsapplication
verification ladder T0 review T1 audit T2 compute T3 formal
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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 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Conv4Rec: A 1-by-1 Convolutional AutoEncoder for User Profiling through Joint Analysis of Implicit and Explicit Feedbacks

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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...

  2. Statistics of Min-max Normalized Eigenvalues in Random Matrices

    cs.LG 2025-12 conditional novelty 4.0 of 10

    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).

  3. Doing More with Less: A Survey on Routing Strategies for Resource Optimisation in Large Language Model-Based Systems

    cs.AI 2025-02 conditional novelty 4.0 of 10

    A survey that classifies LLM routing strategies into pre-generation and post-generation approaches and four implementation families, framed as a performance-cost optimization problem.

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