Next-point-of-interest recommendation based on joint mining of regularity and randomness

Point-of-interest (POI) recommendation is important in location-based applications and has attracted considerable research interest. Despite the inspiring achievements of POI recommendation in recent years, POI recommendation based on the modeling of sparse spatiotemporal data remains challenging, suffering from heterogeneity, randomness, and complexity. In this paper, we propose a novel method for next-POI recommendation. It consists of long- and short-term modules, which learn users’ complex behavior using heterogeneous check-in data. We assume that users have relatively stable regularities on different feature spaces in the long term and randomness can be observed in the users’ decision in the short term, which is influenced by various contexts. Hence, in the long-term module, we design a parallel gated recurrent unit (GRU) network to capture the regularities in the time-space, date-space, geo-space, and activity-space separately from the long trajectory history. In the short-term module, we utilize an attention-based multi-context GRU network to capture the context-aware randomness from the recent trajectory. Furthermore, we integrate the long-term regularity and short-term randomness to model the complex mechanism of human mobility and use the hybrid preference to recommend the next POI. We verify the effectiveness of our model on three public check-in datasets, and experimental results indicate that our approach outperforms state-of-the-art methods for POI recommendation.