A Self-Attention Model for Next Location Prediction Based on Semantic Mining

With the rise in the Internet of Things (IOT), mobile devices and Location-Based Social Network (LBSN), abundant trajectory data have made research on location prediction more popular. The check-in data shared through LBSN hide information related to life patterns, and obtaining this information is helpful for location prediction. However, the trajectory data recorded by mobile devices are different from check-in data that have semantic information. In order to obtain the user’s semantic, relevant studies match the stay point to the nearest Point of Interest (POI), but location error may lead to wrong semantic matching. Therefore, we propose a Self-Attention model for next location prediction based on semantic mining to predict the next location. When calculating the semantic feature of a stay point, the first step is to search for the k-nearest POI, and then use the reciprocal of the distance from the stay point to the k-nearest POI and the number of categories as weights. Finally, we use the probability to express the semantic without losing other important semantic information. Furthermore, this research, combined with sequential pattern mining, can result in richer semantic features. In order to better perceive the trajectory, temporal features learn the periodicity of time series by the sine function. In terms of location features, we build a directed weighted graph and regard the frequency of users visiting locations as the weight, so the location features are rich in contextual information. We then adopt the Self-Attention model to capture long-term dependencies in long trajectory sequences. Experiments in Geolife show that the semantic matching of this study improved by 45.78% in TOP@1 compared with the closest distance search for POI. Compared with the baseline, the model proposed in this study improved by 2.5% in TOP@1.