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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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
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Summary: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 further improving an LF models' convergence rate and prediction accuracy for missing data? To answer this question, this work selects two of representative extended SGD algorithms to propose two novel LF models. Experimental results from two HiDS matrices generated by real recommender systems show that compared standard SGD, extended SGD algorithms enable an LF model to achieve a higher prediction accuracy for missing data of an HiDS matrix, a faster convergence rate, and a larger model diversity.
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ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2019.2891986