Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms
Document Type
Article
Publication Date
2-1-2021
Abstract
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.
Identifier
85099759549 (Scopus)
Publication Title
IEEE Transactions on Systems Man and Cybernetics Systems
External Full Text Location
https://doi.org/10.1109/TSMC.2018.2884191
e-ISSN
21682232
ISSN
21682216
First Page
916
Last Page
926
Issue
2
Volume
51
Grant
cstc2017kjrc-cxcytd0149
Fund Ref
National Natural Science Foundation of China
Recommended Citation
Luo, Xin; Wang, Dexian; Zhou, Meng Chu; and Yuan, Huaqiang, "Latent Factor-Based Recommenders Relying on Extended Stochastic Gradient Descent Algorithms" (2021). Faculty Publications. 4356.
https://digitalcommons.njit.edu/fac_pubs/4356
