Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 8
2334-2339

Collaborative filtering recommendation algorithm based on mixed similarity and differential privacy

Zhang Runliana,b
Zhang Ruia
Wu Xiaoniana
Liu Wenfena
a. Guangxi Key Laboratory of Cryptography & Information Security, b. Guangxi Colleges Key Laboratory of Cloud Computing & Complex Systems, Guilin University of Electronic Technology, Guilin Guangxi 541004, China
DOI: 10.19734/j.issn.1001-3695.2020.12.0542

Abstract

The existing collaborative filtering recommendation algorithms have some problems. Some factors, such as one-sided rating, strong subjectivity, sparsity rating matrix, and so on, which affect the accuracy of recommendation. Moreover, there is privacy leakage in the recommendation. To solve the above problems, this paper proposed a collaborative filtering recommendation algorithm based on the mixed similarity and differential privacy. Firstly, in order to improve the recommendation accuracy, it constructed the mixed similarity based on the weighted calculation for some similarity methods. Then, it used the mixed similarity as the condition for the centroid updating and classification of the K-means algorithm, and clustered users with high similarity to target user by the improved K-means algorithm. Furthermore, the algorithm divided the target users into subsets by the enumeration method, and constructed the utility function based on the mixed similarity. Specifically, for purpose of protecting users' privacy, based on the differential privacy indexing mechanism of utility function, the algorithm selected the neighbor sets from subsets. Finally, the algorithm selected and recommended the items with the highest score from the neighbor sets. The experimental results show that the proposed algorithm can not only protect users' privacy, but also improve the accuracy of recommendation effectively.

Foundation Support

国家自然科学基金资助项目(61862011)
广西自然科学基金资助项目(2018GXNSFAA294036)
广西高校云计算与复杂系统重点实验室项目(YF16205)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.12.0542
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 8
Section: Algorithm Research & Explore
Pages: 2334-2339
Serial Number: 1001-3695(2021)08-015-2334-06

Publish History

[2021-08-05] Printed Article

Cite This Article

张润莲, 张瑞, 武小年, 等. 基于混合相似度和差分隐私的协同过滤推荐算法 [J]. 计算机应用研究, 2021, 38 (8): 2334-2339. (Zhang Runlian, Zhang Rui, Wu Xiaonian, et al. Collaborative filtering recommendation algorithm based on mixed similarity and differential privacy [J]. Application Research of Computers, 2021, 38 (8): 2334-2339. )

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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