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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 39, 2022 No. 7
2058-2062

Sparse matrix interpolation recommendation technology combined with scoring habit weighting

Wen Zuocheng1
Shen Shaopeng1,2
Zhou Xiangbing1,3
Lan Haojie1
Zhang Zhiheng1
1. School of Information & Technology, Sichuan Tourism University, Chengdu 610100, China
2. School of Software Engineering, Chengdu University of Information Technology, Chengdu 610200, China
3. School of Resources & Environment, University of Electronic Science & Technology of China, Chengdu 611731, China
DOI: 10.19734/j.issn.1001-3695.2021.11.0648

Abstract

The imputation-based solution can alleviate the sparsity problem of recommendation system. Improved Jaccard similarity based on logistic user habit rating weighting, which is called the HabJac. By combining this metric, the K nearest neighbor(KNN) obtains the imputation value. Furthermore, this paper proposed a new HISVD recommendation algorithm by combining the regularized singular value decomposition(RSVD) technology to predict the unknown ratings. Firstly, the user's habit rating was the most frequent one. Secondly, logistic weight was positively correlated with Euclidean distance between rating and habit one. The experimental results on four real data sets show that: a) the optimal parameters of HISVD algorithm for different data sets are similar; b) HISVD surpasses the mainstream competitors for the MAE and RSME.

Foundation Support

国家自然科学基金资助项目(62006200)
四川省科技计划项目(2021YFS0407)
四川省科技创新苗子工程项目(2022060)
阿坝州成果转化项目(R21CGZH0001)
中央引导地方科技发展专项(2021ZYD0003)
四川旅游学院校级项目(21SCTUTY05,2021SCTUZK84,2021SCTUZK85,ZL2020024,2020SCTU14)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2021.11.0648
Publish at: Application Research of Computers Printed Article, Vol. 39, 2022 No. 7
Section: Algorithm Research & Explore
Pages: 2058-2062
Serial Number: 1001-3695(2022)07-021-2058-05

Publish History

[2022-02-15] Accepted Paper
[2022-07-05] Printed Article

Cite This Article

温佐承, 沈少朋, 周相兵, 等. 结合评分习惯加权的稀疏矩阵插值推荐技术 [J]. 计算机应用研究, 2022, 39 (7): 2058-2062. (Wen Zuocheng, Shen Shaopeng, Zhou Xiangbing, et al. Sparse matrix interpolation recommendation technology combined with scoring habit weighting [J]. Application Research of Computers, 2022, 39 (7): 2058-2062. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    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.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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