Human trajectory data, collected from various location-based services, is of great significance to the understanding of users. However, trajectory-based user understanding is very challenging, due to the huge semantic gap between the existing low-level GEO spatial information and the target high-level semantic information. In this work, we propose a sequential state model as well as a multi-task based learning method to bridge the above semantic gap. First, we propose a sequential state model to organize the human trajectory data as well as the POI information. Second, we employ a LSTM based representation method to extract the semantic representation from the sequential state model, in which various representations are learned by using the user tags as the supervise information individually. Finally, we devise a multi-task fine-tuning LSTM method to take advantage of the dependency among the tags. We also demonstrate the usage of the proposed method and the effectiveness of the proposal in a real-world demand-side-platform system.

从各种基于位置的服务收集的人体轨迹数据对于理解用户具有重要意义。但是，由于轨迹的用户理解非常困难，这是由于现有的低层GEO空间信息与目标高层语义信息之间存在巨大的语义鸿沟。在这项工作中，我们提出了一种顺序状态模型以及一种基于多任务的学习方法来弥合上述语义鸿沟。首先，我们提出了一种顺序状态模型来组织人体轨迹数据以及POI信息。其次，我们采用基于LSTM的表示方法从顺序状态模型中提取语义表示，其中通过使用用户标签分别作为监督信息来学习各种表示。最后，我们设计了一种多任务微调LSTM方法，以利用标签之间的依赖性。我们还演示了所提出的方法的用法以及该提议在实际需求侧平台系统中的有效性。