Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 2
401-406

Matrix factorization recommendation algorithm combing implicit trust and item correlation

Li Quana
Xu Xinhuab
Liu Xinghonga
Lin Songc
a. College of Computer & Information Engineering, b. College of Teacher Education, c. Dept. of Student Work, Hubei Normal University, Huangshi Hubei 435002, China
DOI: 10.19734/j.issn.1001-3695.2018.07.0530

Abstract

With the development of social network, recommendation system fusing social information solves the problems of cold starting and sparse rating datas of collaborative filtering recommendation system to some extend. Therefore, it still causes problems of making recommendation accuracy decline in the case of the sparse trust datas. Thus, this paper proposed matrix factorization recommendation algorithm combing implicit trust and item correlation. Firstly, it decomposed the trust datas by the matrix factorization model, and obtained the implicit trusted matrix of users. It introduced the influence of users on this basis, and proposed the recommendation model based on implicit trust. Secondly, it proposed the recommendation model based on item correlation in order to make better use of correlation information between items and reflect on orientation between items. Finally, it combined the two kinds of recommendation models, and proposed the TCRMF algorithm. The experimental results show that the proposed algorithm still improves the accuracy of recommendation algorithm effectively in the conditions that score and trust datas are sparse, and has a good application prospect.

Foundation Support

湖北省教育厅科技项目(B2018150)
国家档案局科技计划项目(2016-x-51)
湖北省教育科学“十二五”规划项目(2011B130)
湖北省高等学校优秀中青年科技创新团队计划项目(T201515)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.07.0530
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 2
Section: Algorithm Research & Explore
Pages: 401-406
Serial Number: 1001-3695(2020)02-017-0401-06

Publish History

[2020-02-05] Printed Article

Cite This Article

李全, 许新华, 刘兴红, 等. 融合隐含信任度和项目关联度的矩阵分解推荐算法 [J]. 计算机应用研究, 2020, 37 (2): 401-406. (Li Quan, Xu Xinhua, Liu Xinghong, et al. Matrix factorization recommendation algorithm combing implicit trust and item correlation [J]. Application Research of Computers, 2020, 37 (2): 401-406. )

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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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