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
Recommended Citation
Luo, Xin; Zhou, Yue; Liu, Zhigang; and Zhou, Mengchu, "Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems" (2023). Faculty Publications. 1825.
https://digitalcommons.njit.edu/fac_pubs/1825