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

摘要/Abstract

摘要：

为了提高动态推荐效果, 从时间个性化和连续性的角度出发, 细化了签到用户的时间特征, 利用灰关联分析度量时间向量的相似度, 与矩阵分解算法结合, 给出了一种新的矩阵分解算法。该算法可缓解时间戳细化签到矩阵后带来的数据稀疏的影响。同时为了提高个性化推荐, 采用自适应核密度估计方法捕捉用户的空间偏好, 增强用户的个性化体验, 进而提高推荐质量。在此基础上, 设计了一种新的兴趣点推荐算法。实验结果表明, 该算法能有效地提高推荐准确率和召回率。

关键词: 兴趣点推荐, 灰关联分析, 矩阵分解, 自适应核密度估计

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

中图分类号:

TP311

陈江美, 张文德. 基于灰关联分析和时空偏好特征的兴趣点推荐算法[J]. 系统工程与电子技术, 2022, 44(6): 1934-1941.

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.

图/表 5

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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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