Studies on the checked-in point-of-interests have become an important means to learn user’s behavior. Nevertheless, users do not sign in to all visited locations. There are unobserved check-in locations in the generated POI trajectory. Such the trajectory is called an incomplete trajectory, and unobserved point is called missing point. However, incomplete trajectory has a negative impact on downstream tasks such as personalized recommendation system, criminal identification and next location prediction. It is a challenge to use the forward sequence and backward sequence information of the missing point to complete the missing POI. Therefore, we propose a bidirectional model based on mask and attention mechanism (BMAM) to solve the problem of missing POI completion in user’s incomplete trajectory. The context information of trajectory checked in by user can be mined to connect the missing POI with the forward sequence and backward sequence information. Therefore, the model learns the order dependence between each location according to the user trajectory sequence and obtain the user’s dynamic preference to identify the missing POI in the sequence. Besides, the attention mechanism is used to improve the user's representation feature, that is, the preference for POI categories. The experimental results demonstrate that our BMAM outperforms the state-of-the-art models for completion on missing POI of user’s incomplete sequence.

对签到兴趣点的研究已成为了解用户行为的重要手段。然而，用户不会登录到所有访问过的位置。生成的 POI 轨迹中有未观察到的签到位置。这样的轨迹称为不完全轨迹，未观测到的点称为缺失点。然而，不完整的轨迹对个性化推荐系统、犯罪识别和下一个位置预测等下游任务有负面影响。利用缺失点的前向序列和后向序列信息来完成缺失的 POI 是一个挑战。因此，我们提出了一种基于掩码和注意力机制（BMAM）的双向模型来解决用户轨迹不完整时丢失POI完成的问题。可以挖掘用户签入轨迹的上下文信息，将丢失的POI与前向序列和后向序列信息联系起来。因此，模型根据用户轨迹序列学习每个位置之间的顺序依赖关系，获取用户的动态偏好，以识别序列中缺失的 POI。此外，注意力机制用于提高用户的表示特征，即对 POI 类别的偏好。实验结果表明，我们的 BMAM 在完成用户不完整序列的丢失 POI 方面优于最先进的模型。该模型根据用户轨迹序列学习每个位置之间的顺序依赖关系，获得用户的动态偏好，以识别序列中缺失的POI。此外，注意力机制用于提高用户的表示特征，即对 POI 类别的偏好。实验结果表明，我们的 BMAM 在完成用户不完整序列的丢失 POI 方面优于最先进的模型。该模型根据用户轨迹序列学习每个位置之间的顺序依赖关系，获得用户的动态偏好，以识别序列中缺失的POI。此外，注意力机制用于提高用户的表示特征，即对 POI 类别的偏好。实验结果表明，我们的 BMAM 在完成用户不完整序列的丢失 POI 方面优于最先进的模型。