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Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 37, 2020 No. 9
2615-2619,2639

Collaborative filtering recommendation algorithm based on improved Canopy clustering

Tang Zekun1
Huang Bingqing2
Li Lian1
1. School of Information Science & Engineering, Lanzhou University, Lanzhou 730000, China
2. School of Science, Rensselaer Polytechnic Institute, USA
DOI: 10.19734/j.issn.1001-3695.2019.04.0137

Abstract

By establishing the binary relationship between users and information products, the recommender system makes use of the data generated by user behavior to mine the objects that each user is interested in and make recommendations, user-based collaborative filtering has been a mainstream approach in recent years, but it has a limitation: recommendations need to consider all users, and a specific user is often similar to a small number of users. To solve this problem, this paper proposed a collaborative filtering algorithm based on improved Canopy clustering, which combined the user model data density, distance and user activity to calculate the weights of the user, then clustered the user model data, the idea of clustering based on Canopy made one user could belong to different classes, which fit in with situations that users might be interested in multiple areas. Finally, corresponding recommendations were made for each user in Canopy, and it predicted the objects that users might be interested in based on the clustering result and user score. By comparing with other algorithms on two real-world data sets MovieLens and million songs, it verifies that the proposed algorithm can significantly improve the accuracy of the recommender system.

Foundation Support

国家重点研发计划资助项目(2018YFB1003205)
国家自然科学基金资助项目(61300230,61370219)
甘肃省自然科学基金资助项目(1107RJZA188)
甘肃省科技支撑计划资助项目(1104GKCA037)
甘肃省科技重大专项资助项目(1102FKDA010)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2019.04.0137
Publish at: Application Research of Computers Printed Article, Vol. 37, 2020 No. 9
Section: Algorithm Research & Explore
Pages: 2615-2619,2639
Serial Number: 1001-3695(2020)09-010-2615-05

Publish History

[2020-09-05] Printed Article

Cite This Article

唐泽坤, 黄柄清, 李廉. 基于改进Canopy聚类的协同过滤推荐算法 [J]. 计算机应用研究, 2020, 37 (9): 2615-2619,2639. (Tang Zekun, Huang Bingqing, Li Lian. Collaborative filtering recommendation algorithm based on improved Canopy clustering [J]. Application Research of Computers, 2020, 37 (9): 2615-2619,2639. )

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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