Effects of Extended Stochastic Gradient Descent Algorithms on Improving Latent Factor-Based Recommender Systems

High-dimensional and sparse (HiDS) matrices from recommender systems contain various useful patterns. A latent factor (LF) analysis is highly efficient in grasping these patterns. Stochastic gradient descent (SGD) is a widely adopted algorithm to train an LF model. Can its extensions be capable of f...

Full description

Bibliographic Details

Published in:	IEEE robotics and automation letters Vol. 4; no. 2; pp. 618 - 624
Main Authors:	Luo, Xin, Zhou, MengChu
Format:	Journal Article
Language:	English
Published:	Piscataway IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	AI-based methods Algorithms Big data in robotics and automation Convergence Data models machine learning Missing data Model accuracy Prediction algorithms Predictive models Recommender systems Stochastic processes
ISSN:	2377-3766, 2377-3766
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!