IeMTLF: Interaction-enhanced Multi-Task Learning Framework for next location prediction

Abstract

Location-Based Services (LBSs) provide spatial and semantic information of various locations, thanks to the advances in mobile devices and commercial map services. However, most existing studies on next location prediction neglect the semantic information and do not investigate the effects of different levels of semantic granularity. To address these issues, we propose an Interaction-enhanced Multi-Task Learning Framework (IeMTLF) that jointly predicts the next location and its semantic category and leverages higher-order feature interactions to enrich the semantic learning process. Moreover, we examine the effect of two different levels of semantic granularity, namely, specific and abstract, on prediction performance. We conducted extensive experiments on two real-world datasets and demonstrate that IeMTLF outperforms seven baselines on all evaluation metrics, with a maximum gain of more than 13% for Acc@5 compared to the state-of-the-art method. Our implementation code is available at https://github.com/mrc-wyh/IeMTLF.