In accordance with regulations and requirements, the editorial department's website domain has been changed to arocmag.cn. The original domain (arocmag.com) will be discontinued after Dec. 31st, 2024.
See More
Algorithm Research & Explore
|
App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 3
680-683

Collaborative filtering algorithm combined with score scale factor and item attribute

Li Shuzhia
Li Zhijuna
Deng Xiaohongb
a. College of Information Engineering, b. College of Applied Science, Jiangxi University of Science & Technology, Ganzhou Jiangxi 341000, China
DOI: 10.19734/j.issn.1001-3695.2018.08.0627

Abstract

There exists several issues in traditional collaborative filtering algorithms, which has the sparsity of user rating matrix and ignores the relationship between item attributes. Considering above problems, this paper proposed a novel collaborative filtering algorithm combining score ratio factor and item attribute. This algorithm used the scoring matrix to obtain the ratio matrix of common and non-common score users between items. Therefore, it increased the influence degree of the users of the item common score, and reduced the error caused by the sparsity of the user-item scoring matrix on the item similarity calculation. It quantified the item attribute to obtain the weight of the item similarity, and it also improved the accuracy of the item similarity calculation. According to the above two points, this paper proposed an algorithm combining scoring scale factor and item attribute weight as item similarity weight. Experimental results show that the proposed algorithm improves the recall rate and accuracy by 5.1% and 4.7% respectively compared with the existing methods. The algorithm is suitable for personalized recommendation of e-commerce websites.

Foundation Support

国家自然科学基金资助项目(61762046)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.08.0627
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 3
Section: Algorithm Research & Explore
Pages: 680-683
Serial Number: 1001-3695(2020)03-009-0680-04

Publish History

[2020-03-05] Printed Article

Cite This Article

李淑芝, 李志军, 邓小鸿. 结合评分比例因子及项目属性的协同过滤算法 [J]. 计算机应用研究, 2020, 37 (3): 680-683. (Li Shuzhi, Li Zhijun, Deng Xiaohong. Collaborative filtering algorithm combined with score scale factor and item attribute [J]. Application Research of Computers, 2020, 37 (3): 680-683. )

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.

Indexed & Evaluation

  • The Second National Periodical Award 100 Key Journals
  • Double Effect Journal of China Journal Formation
  • the Core Journal of China (Peking University 2023 Edition)
  • the Core Journal for Science
  • Chinese Science Citation Database (CSCD) Source Journals
  • RCCSE Chinese Core Academic Journals
  • Journal of China Computer Federation
  • 2020-2022 The World Journal Clout Index (WJCI) Report of Scientific and Technological Periodicals
  • Full-text Source Journal of China Science and Technology Periodicals Database
  • Source Journal of China Academic Journals Comprehensive Evaluation Database
  • Source Journals of China Academic Journals (CD-ROM Version), China Journal Network
  • 2017-2019 China Outstanding Academic Journals with International Influence (Natural Science and Engineering Technology)
  • Source Journal of Top Academic Papers (F5000) Program of China's Excellent Science and Technology Journals
  • Source Journal of China Engineering Technology Electronic Information Network and Electronic Technology Literature Database
  • Source Journal of British Science Digest (INSPEC)
  • Japan Science and Technology Agency (JST) Source Journal
  • Russian Journal of Abstracts (AJ, VINITI) Source Journals
  • Full-text Journal of EBSCO, USA
  • Cambridge Scientific Abstracts (Natural Sciences) (CSA(NS)) core journals
  • Poland Copernicus Index (IC)
  • Ulrichsweb (USA)