[1]赵学健,张雨豪,陈 昊,等.基于 FCM 用户聚类的协同过滤推荐算法[J].计算机技术与发展,2021,31(08):6-12.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 002]
　ZHAO Xue-jian,ZHANG Yu-hao,CHEN Hao,et al.Collaborative Filtering Recommendation Algorithm Based on Fuzzy C-Means User Clustering[J].,2021,31(08):6-12.[doi:10. 3969 / j. issn. 1673-629X. 2021. 08. 002]
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基于 FCM 用户聚类的协同过滤推荐算法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年08期
页码:
6-12
栏目:
大数据分析与挖掘
出版日期:
2021-08-10

文章信息/Info

Title:
Collaborative Filtering Recommendation Algorithm Based on Fuzzy C-Means User Clustering
文章编号:
1673-629X(2021)08-0006-07
作者:
赵学健1; 张雨豪1; 陈 昊1; 刘 旭2; 李朋起3
1. 南京邮电大学 现代邮政学院,江苏 南京 210003;
2. 南京邮电大学 通信与信息工程学院,江苏 南京 210003;
3. 南京邮电大学 物联网学院,江苏 南京 210003
Author(s):
ZHAO Xue-jian1; ZHANG Yu-hao1; CHEN Hao1; LIU Xu2; LI Peng-qi3
1. School of Modern Posts,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;
2. School of Telecommunications & Information Engineering,Nanjing University of Posts andTelecommunications, Nanjing 210003,China;
3. School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
推荐算法; 协同过滤; 模糊 C 均值聚类; 遗传算法; 评分矩阵
Keywords:
recommendation algorithm; collaborative filtering; fuzzy c-means clustering; genetic algorithm; rating matrix
分类号:
TP391
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 08. 002
摘要:
传统的协同过滤推荐算法存在数据稀疏性以及推荐准确率低等问题,针对该问题提出一种基于模糊 C 均值聚类的协同过滤推荐算法 GAFCM-CF(genetic algorithm based fuzzy c-means collaborative filtering)。 首先,该算法结合用户评分和项目特征构建用户特征偏好矩阵,深入挖掘利用用户隐藏信息。 其次, 该算法通过模糊 C 均值聚类算法对用户进行聚类,并且为了防止模糊 C 均值聚类算法收敛于局部极小值,影响推荐质量,该算法基于遗传算法对模糊 C 均值聚类算法进行了改进,防止出现局部最优解。 最后,该算法综合考虑了用户特征偏好矩阵以及用户项目评分矩阵计算用户相似度,实现推荐。 实验结果表明,所提出的基于改进模糊 C 均值聚类的协同过滤推荐算法相比于传统的基于用户的协同过滤推荐算法及 PDSFCM 算法具有更好的推荐质量,提高了推荐的准确率。
Abstract:
Aiming at the problem of data sparsity and low accuracy of traditional collaborative filtering recommendation algorithms,a new genetic algorithm based fuzzy c-means collaborative filtering recommendation algorithm named GAFCM-CF is proposed. Firstly,the user feature preference matrix is constructed based on user rating matrix? and item characteristics,and the hidden information of users is deeply mined. Secondly,the fuzzy c-means clustering algorithm is used to cluster the users. In order to prevent the fuzzy c-means clustering algorithm from converging to the local minimum and affecting the recommendation quality,the proposed algorithm improves the fuzzy c-means clustering algorithm based on genetic algorithm to prevent the local optimal solution. Finally,the user similarity is calculated by considering both the? ? ? user characteristic preference matrix and user item rating matrix to realize better recommendation. The experiment shows that the proposed collaborative filtering recommendation algorithm based on improved FCM has better recommendation quality and improves the accuracy of recommendation compared with the traditional user-based collaborative filtering recommendation al鄄gorithms and PDSFCM algorithm.

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更新日期/Last Update: 2021-08-10