基于图神经网络的用户轨迹分类

Trajectory-User Classification with Graph Neural Network

针对现有轨迹用户链接(TUL)算法对轨迹信息提取不充分、计算成本过高等问题,该文提出了一种新的基于图神经网络(GNN)的TUL算法。首先,利用轨迹中的签到点构建签到图;其次,在签到图的基础上使用图神经网络学习签到图中的节点嵌入,保存签到点的位置信息和用户的访问偏好信息;最后,利用循环神经网络(RNN)构建轨迹序列的向量表示,并使用全连接网络对轨迹进行用户分类,实现轨迹与用户链接。实验结果表明,相比于传统的用户轨迹分类算法,该方法能更有效地挖掘用户轨迹的潜在移动规律,显著提高了两个数据集上的链接准确性和学习效率。

To address the insufficient data issue and the high computational cost of existing algorithms,we present a new TUL model based on graph neural network(GNN).More specifically,the check-in graph is constructed using the check-in points in trajectories,based on which we use a graph neural network to learn the check-in embeddings in the graph,which could preserve users'check-in preference and spatio-temporal visiting patterns in a graph representation learning manner.Subsequently,the check-in representations in the trajectory are fed into a recurrent neural network,followed by a fully connected network,to learn the sequential dependencies of visits while distinguishing different users'trajectories.Experimental evaluations conducted on benchmark datasets show that our method can better capture the underlying moving patterns of users'trajectories more effectively compared with the previous TUL algorithms.Furthermore,the user linking accuracy and learning efficiency are significantly improved compared with the existing methods.