Spatio-Temporal Transformer Recommender: Next Location Recommendation with Attention Mechanism by Mining the Spatio-Temporal Relationship between Visited Locations

Abstract

Location-based social networks (LBSN) allow users to socialize with friends by sharing their daily life experiences online. In particular, a large amount of check-ins data generated by LBSNs capture the visit locations of users and open a new line of research of spatio-temporal big data, i.e., the next point-of-interest (POI) recommendation. At present, while some advanced methods have been proposed for POI recommendation, existing work only leverages the temporal information of two consecutive LBSN check-ins. Specifically, these methods only focus on adjacent visit sequences but ignore non-contiguous visits, while these visits can be important in understanding the spatio-temporal correlation within the trajectory. In order to fully mine this non-contiguous visit information, we propose a multi-layer Spatio-Temporal deep learning attention model for POI recommendation, Spatio-Temporal Transformer Recommender (STTF-Recommender). To incorporate the spatio-temporal patterns, we encode the information in the user’s trajectory as latent representations into their embeddings before feeding them. To mine the spatio-temporal relationship between any two visited locations, we utilize the Transformer aggregation layer. To match the most plausible candidates from all locations, we develop on an attention matcher based on the attention mechanism. The STTF-Recommender was evaluated with two real-world datasets, and the findings showed that STTF improves at least 13.75% in the mean value of the Recall index at different scales compared with the state-of-the-art models.