基于灰关联分析和时空偏好特征的兴趣点推荐算法

doi:10.12305/j.issn.1001-506X.2022.06.20

Abstract

Abstract:

To improve the effect of dynamic recommendation, the time characteristics from the perspective of temporal non-uniformness and consecutiveness is refined. The similarity of time vectors is measured using grey relational analysis (GRA) and incorporated with the matrix factorization algorithm. A new matrix decomposition algorithm is proposed, which can alleviate the data sparsity caused by dividing the check-in matrix with time slots. To achieve personalized recommendation, the adaptive kernel density estimation is leveraged to capture the personalized spatial preference, and thus enhance the recommendation quality. On this basis, a novel point-of-interest (POI) recommendation algorithm is designed. Experiment results show the proposed algorithm can effectively improve the precision and recall.

Key words: point-of-interest (POI) recommendation, grey relational analysis (GRA), matrix factorization, adaptive kernel density estimation

CLC Number:

TP311

Jiangmei CHEN, Wende ZHANG. Point-of-interest recommendation algorithm based on grey relational analysis and temporal-spatial preference feature[J]. Systems Engineering and Electronics, 2022, 44(6): 1934-1941.

Figures/Tables 5

References 30

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Foursquare数据集	数据
用户数	2 160
兴趣点数	5 570
签到记录	192 507
平均签到数	89.12

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Point-of-interest recommendation algorithm based on grey relational analysis and temporal-spatial preference feature

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