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...

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Veröffentlicht in:	IEEE robotics and automation letters Jg. 4; H. 2; S. 618 - 624
Hauptverfasser:	Luo, Xin, Zhou, MengChu
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 01.04.2019 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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-Zugang:	Volltext
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