Location recommendation algorithm based on K-means and matrix factorization

被引：0
|
作者：
Li B. [1 ]
Zhou X. [2 ]
Mei F. [3 ]
Pan S.-N. [4 ]
机构：
[1] College of Mathematics, Jilin University, Changchun
[2] Center for Computer Fundamental Education, Jilin University, Changchun
[3] College of Computer Science and Technology, Jilin University, Changchun
[4] College of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan
来源：
Jilin Daxue Xuebao (Gongxueban)/Journal of Jilin University (Engineering and Technology Edition) | 2019年 / 49卷 / 05期
关键词：
Computer application; K-means clustering; Location based social network; Location recommendation; Matrix factorization;
D O I：
10.13229/j.cnki.jdxbgxb20181264
中图分类号：
学科分类号：
摘要：
In this paper, a novel POI recommendation algorithm based on clustering and matric factorization is proposed. The matrix factorization method is used to quantify the user's number of check-in for unknown location. The clustering method is used to divide the users into groups. In addition, the improved similarity calculation method is used to calculate the similarity among users. The experiment results on Yelp dataset show that the proposed method can improve the location recommendation recall rate and accuracy rate, demonstrating the better performance of our method compared to other methods. © 2019, Jilin University Press. All right reserved.
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页码：1653 / 1660
页数：7
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