"Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensio" by Xin Luo, Yue Zhou et al.
 

Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems

Document Type

Article

Publication Date

4-1-2023

Abstract

A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and Momentum-incorporated Update (SLF-NM2U) algorithm, which enables its fast convergence. It is crucial to achieve a rigorously theoretical proof regarding its fast convergence, which has not been provided in prior research. Aiming at addressing this critical issue, this work theoretically proves that with an appropriately chosen momentum coefficient, SLF-NM2U enables the fast convergence of an FNLF model in both continuous and discrete time cases. Empirical analysis of HiDS matrices generated by representative industrial applications provides empirical evidences for the theoretical proof. Hence, this study represents an important milestone in the field of HiDS matrix analysis.

Identifier

85118671276 (Scopus)

Publication Title

IEEE Transactions on Knowledge and Data Engineering

External Full Text Location

https://doi.org/10.1109/TKDE.2021.3125252

e-ISSN

15582191

ISSN

10414347

First Page

3897

Last Page

3911

Issue

4

Volume

35

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