GNN-based long and short term preference modeling for next-location prediction

Abstract

Next-location prediction is a special task of the next POIs recommendation. Different from general recommendation tasks, next-location prediction is highly context-dependent: (1) sequential dependency, i.e., the sequential locations checked in by a user have high correlation; (2) temporal dependency, i.e. check-in preferences are identified as different days or nights; (3) spatial dependency, i.e. users prefer to visit closer locations. Recent studies have been very successful in predicting users' next location by comprehensively considering user preferences. Nonetheless, these methods not only fail to capture temporal dependencies but also fail to capture location topology information. To fill this gap, we propose a GNN-based model which converts POIs into a low-dimensional metric and integrates users' long-term and short-term preferences to comprehensively represent dynamic preferences. The model consists of graph neural networks for long-term preference modeling and LSTM for short-term preference modeling. Comprehensive experiments are conducted on two real-world datasets, and results demonstrate the effectiveness of our approach over state-of-the-art methods for next-location prediction.