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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 9
2609-2612,2617

Collaborative filtering recommendation algorithm combined with user interest degree clustering

Huang Xianying
Long Shuyan
Xie Jin
School of Computer Science & Engineering, Chongqing University of Technology, Chongqing 400054, China
DOI: 10.19734/j.issn.1001-3695.2018.03.0149

Abstract

Aiming at the problem of ignores the user's interest in the key words and the data sparseness in traditional collaborative filtering algorithm. This paper proposed a collaborative filtering recommendation algorithm combined with the user interest degree clustering, and used user ratings for projects and extracting keywords from item attributes. It proposed a new rating frequency-inverse item frequency algorithm. According to the target users' scoring frequency for a key word and the frequency of the keyword being evaluated by all users, it got users' preferences for keywords, formed user preference matrix, and clustered on the basis of this matrix. Then it used logistic function to get users' interest in projects, cleared user preferences and found similar users of target users in the clusters. Then extracted N items from neighbors' preferences, and recommended users. Experimental results show that the algorithm accuracy rate is always better than the traditional algorithm. It's more accurate to judge the user interest, alleviating the problem of data sparseness, and effectively improves the accuracy and efficiency of re-commendation.

Foundation Support

国家社会科学基金资助项目(17XXW004)
国家自然科学基金资助项目(61603065)
国家统计局全国统计科学研究重点项目(2016LZ08)
国家教育部人文社会科学研究项目(15YJC790061)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2018.03.0149
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 9
Section: Algorithm Research & Explore
Pages: 2609-2612,2617
Serial Number: 1001-3695(2019)09-011-2609-04

Publish History

[2019-09-05] Printed Article

Cite This Article

黄贤英, 龙姝言, 谢晋. 结合用户兴趣度聚类的协同过滤推荐算法 [J]. 计算机应用研究, 2019, 36 (9): 2609-2612,2617. (Huang Xianying, Long Shuyan, Xie Jin. Collaborative filtering recommendation algorithm combined with user interest degree clustering [J]. Application Research of Computers, 2019, 36 (9): 2609-2612,2617. )

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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