中文版 | English
CSpace
Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms
Luo, Xin1; Wang, Dexian2,3; Zhou, MengChu4,5; Yuan, Huaqiang1
2021-02-01
摘要High-dimensional and sparse (HiDS) matrices generated by recommender systems contain rich knowledge regarding various desired patterns like users' potential preferences and community tendency. Latent factor (LF) analysis proves to be highly efficient in extracting such knowledge from an HiDS matrix efficiently. Stochastic gradient descent (SGD) is a highly efficient algorithm for building an LF model. However, current LF models mostly adopt a standard SGD algorithm. Can SGD be extended from various aspects in order to improve the resultant models' convergence rate and prediction accuracy for missing data? Are such SGD extensions compatible with an LF model? To answer them, this paper carefully investigates eight extended SGD algorithms to propose eight novel LF models. Experimental results on two HiDS matrices generated by real recommender systems show that compared with an LF model with a standard SGD algorithm, an LF model with extended ones can achieve: 1) higher prediction accuracy for missing data; 2) faster convergence rate; and 3) model diversity.
关键词Big data bi-linear collaborative filtering (CF) high-dimensional and sparse (HiDS) matrix industry latent factor (LF) analysis missing data recommender system
DOI10.1109/TSMC.2018.2884191
发表期刊IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS
ISSN2168-2216
卷号51期号:2页码:916-926
通讯作者Zhou, MengChu(zhou@njit.edu) ; Yuan, Huaqiang(yuanhq@dgut.edu.cn)
收录类别SCI
WOS记录号WOS:000608693000024
语种英语