Abstract: Trajectories do not only record users’ location histories in the physical world but also imply their personal interests and behavior patterns. This paper mainly focuses on the stay region in the trajectory, to find user similarity from the spatio-temporal overlaps of the stay region and propose a method to calculate the user similarity. It detects the stay region, then represents the stay region of the trajectory by using minimum bounding boxes, a similarity measure formula is proposed based on the Longest Common Subsequence algorithm（LCS）. An experiment is performed to evaluate the performance of the new similarity measure by using the trajectories of 60 users in a period of 6 weeks, the results show that the method has high accuracy.

Key words: user similarity, trajectory similarity, longest common subsequence, spatio-temporal data mining

摘要： 移动用户的位置轨迹中蕴含着用户的运动规律，行为模式等丰富的信息。重点关注用户轨迹中的停留区域，从轨迹间共同停留区域的时空重叠中挖掘用户行为兴趣的相似性，提出一种基于最长公共子序列的用户时空行为兴趣相似性计算方法。提取轨迹中的停留区域，利用最小包围盒技术描述轨迹中的停留区域，结合最长公共子序列算法提出一种基于最长公共子包围盒长度的用户相似性计算方法。实验收集60个志愿者6周的真实时空轨迹数据来评价该方法，实验结果表明该方法具有较高的准确率。

关键词: 用户相似性, 轨迹相似性, 最长公共子序列, 时空挖掘