基于灰关联分析和时空偏好特征的兴趣点推荐算法

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

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。

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.