The Missing POI Completion Based on Bidirectional Masked Trajectory Model

Abstract

With the development of location-based social networks (LBSNs), users can check in Point-of-Interest (POIs) at any time. However, users do not check in all places they have visited, so the POI trajectory sequence generated through LBSNs is incomplete. An incomplete POI trajectory will have a negative impact on subsequent tasks such as POI recommendation and next POI prediction. Therefore, we complete the missing POI in the user trajectory sequence. Since the POI trajectory sequence is incomplete, it is a challenge to use the pre-order and post-order trajectory sequences with missing POIs. Therefore, we propose a masked POI trajectory model (MPTM) that uses the bidirectionality of BERT to complete the missing POIs in user’s behavior sequence. By masking the missing POIs, MPTM fully explores the relationship between the missing POIs and the known POIs to predict the missing POIs. In order to strengthen the relationship between POIs in the user trajectory sequence, we build a graph for each user’s incomplete POIs sequence to explore the user’s hidden behavior habits. Besides, we design experiments to explore the relationship between the continuity of the number of missing POIs and the predictive ability of the model. The experimental results demonstrate that our MPTM outperforms the state-of-the-art models for completion on missing POIs of user’s behavior sequence.