Algorithm Research & Explore
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696-701

Collaborative filtering recommendation algorithm based on item fuzzy similarity

Wang Sen
Chen Li
Zhang Jie
School of Information Science & Technology, Northwest University, Xi'an 710127, China

Abstract

In view of the problem of fuzziness of rating and tag in traditional collaborative filtering algorithms, this paper used trapezoidal fuzzy number to describe the mapping relationship between rating and satisfaction. The algorithm considered the impact of sparseness of the rating, constructed a new trapezoidal fuzzy rating model to determine the similarity based on fuzzy rating, analyzed the degree of membership between the tag and the item, and constructed a fuzzy item-tag matrix to measure the similarity based on the degree of tag membership. Finally, it used the improved scoring prediction strategy to estimate the score. The experimental results on the MovieLens dataset show that the proposed algorithm improves the prediction accuracy while suppressing the cold start of the project, alleviating the problems of fuzziness and sparseness, which indicates the effectiveness of the proposed algorithm.

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.04.0056
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 3
Section: Algorithm Research & Explore
Pages: 696-701
Serial Number: 1001-3695(2021)03-010-0696-06

Publish History

[2021-03-05] Printed Article

Cite This Article

王森, 陈莉, 张洁. 基于项目模糊相似度的协同过滤推荐算法 [J]. 计算机应用研究, 2021, 38 (3): 696-701. (Wang Sen, Chen Li, Zhang Jie. Collaborative filtering recommendation algorithm based on item fuzzy similarity [J]. Application Research of Computers, 2021, 38 (3): 696-701. )

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

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