作　　者： (殷浩腾）; (刘洋）;

机构地区： 山东大学计算机科学与技术学院,济南250101 北京大学前沿交叉学科研究院,北京100871

出　　处： 《中国科学：信息科学》 2017年第8期1051-1065,共15页

摘　　要： 时空数据具有多维关联特性,而深度学习恰恰因其能够对复杂高维数据进行高层抽象处理而备受关注.本文依据轨迹数据特征给出其形式化定义,并据此构建基于Word2vec的时空语义轨迹模型.通过模型网络训练位置特征向量,对不同时间粒度下的用户轨迹进行语义探究.实验中采取Top-K近邻预测和聚类分析等手段验证了轨迹模型在无监督式学习下输出的位置向量具备空间语义且定型良好.其结果也进一步检验了基于词向量的语言模型迁移至轨迹挖掘的研究具备可行性. Spatial temporal data has associated multidimensional features. Deep learning has attracted much attention due to its ability to perform high-level abstraction of complex data. In this paper, we give the definition of the track-data based on its characteristics, and build a spatial temporal semantic trajectory model using Word2 vec as its foundation. We explore the semantics of different user-tracks under varying time periods by training position vectors in the model network. During the experiments, we use Top-K neighbor prediction and cluster analysis to verify that the position vector has both good semantic meaning and structure. The vector is derived from a trajectory model that employs unsupervised learning. The results also test a word-vector-based language model that can be applied to the study of trajectory mining.

关 键 词： 社交网络 时空轨迹 语义分析 特征向量 深度学习