BMAM: complete the missing POI in the incomplete trajectory via mask and bidirectional attention model

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.