Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 2
370-374

Accelerating recommendation algorithm using fitting matrix and two orders fusion iterative

Wang Shuai
Sun Fuzhen
Wang Shaoqing
Zhang Jin
Fang Chun
College of Computer Science & Technology, Shandong University of Technology, Zibo Shandong 255049, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0533

Abstract

The traditional matrix decomposition model cannot fully explored the intrinsic relationship between the user and the object in the mean, bias and characteristics. This paper proposed a fitting matrix model to improve the prediction performance by constructing the user and the item matrix to represent the characteristics of the user and the item respectively. The matrix decomposition model had the advantage of accuracy in the field of recommender system, but the gradient descent method, which was the most popular method to train parameters of model, had a slow convergence speed. To resolve the above defects, this paper considered to accelerate the convergence speed using the convergence of quasi Newton method, and named the proposed algorithm as fitting matrix and two orders fusion iterative(FAST) algorithm. The experimental results show that the FAST algorithm is better than the traditional non negative matrix decomposition(NMF), singular value matrix decomposition(SVD), and the regularized singular value matrix decomposition(RSVD). FAST algorithm has a decrease with regard to the mean absolute error(MAE) and the root mean square error(RMSE), and has a significant improvement in the iterative efficiency, which alleviates the problem that the accuracy is difficult to balance with the efficiency of the iteration.

Foundation Support

国家自然科学基金资助项目(61602280)
山东省自然科学基金资助项目(ZR2014FQ028)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0533
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 2
Section: Algorithm Research & Explore
Pages: 370-374
Serial Number: 1001-3695(2020)02-011-0370-05

Publish History

[2020-02-05] Printed Article

Cite This Article

王帅, 孙福振, 王绍卿, 等. 拟合矩阵与两阶融合迭代加速推荐算法 [J]. 计算机应用研究, 2020, 37 (2): 370-374. (Wang Shuai, Sun Fuzhen, Wang Shaoqing, et al. Accelerating recommendation algorithm using fitting matrix and two orders fusion iterative [J]. Application Research of Computers, 2020, 37 (2): 370-374. )

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  • Application Research of Computers Monthly Journal
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    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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