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	Large-Scale and Scalable Latent Factor Analysis via Distributed Alternative Stochastic Gradient Descent for Recommender Systems
	Shi, Xiaoyu1,2; He, Qiang5; Luo, Xin1,2,3,4; Bai, Yanan1,2; Shang, Mingsheng1,2
	2022-04-01
摘要	Latent factor analysis (LFA) via stochastic gradient descent (SGD) is highly efficient in discovering user and item patterns from high-dimensional and sparse (HiDS) matrices from recommender systems. However, most LFA-based recommender systems adopt a standard SGD algorithm, which suffers limited scalability when addressing big data. On the other hand, most existing parallel SGD solvers are either under the memory-sharing framework designed for a bare machine or suffering high communicational costs, which also greatly limits their applications in large-scale systems. To address the above issues, this article proposes a distributed alternative stochastic gradient descent (DASGD) solver for an LFA-based recommender. Its training-dependences among latent features are decoupled via alternatively fixing one-half of the features to learn the other half following the principle of SGD but in parallel. It's distribution mechanism consists of efficient data partition, allocation and task parallelization strategies, which greatly reduces its communicational cost for high scalability. Experimental results on three large-scale HiDS matrices generated by real-world applications demonstrate that the proposed DASGD algorithm outperforms state-of-the-art distributed SGD solvers for recommender systems in terms of prediction accuracy as well as scalability. Hence, it is highly useful for training LFA-based recommenders on large scale HiDS matrices with the help of cloud computing facilities.
关键词	Recommender systems Training Optimization Big Data Cloud computing Computational modeling Sparse matrices Recommender system latent factor analysis high-dimensional and sparse matrices alternative stochastic gradient descent distributed computing
DOI	10.1109/TBDATA.2020.2973141
发表期刊	IEEE TRANSACTIONS ON BIG DATA
ISSN	2332-7790
卷号	8期号:2页码:420-431
通讯作者	Luo, Xin(luoxin21@cigit.ac.cn)
收录类别	SCI
WOS记录号	WOS:000767848400009
语种	英语