Next-POI Recommendation via Spatial-Temporal Knowledge Graph Contrastive Learning and Trajectory Prompt

Next POI (Point-of-Interest) recommendation aims to forecast users’ future movements based on their historical check-in trajectories, holding significant value in location-based services. Existing methods address trajectory data sparsity by integrating rich auxiliary information or using spatial-temporal knowledge graphs (STKGs), showing promising results. Yet, they face two main challenges: i) Due to the difficulty of transforming structured trajectory data into trajectory text describing users’ spatial-temporal mobility, the powerful reasoning ability of pre-trained language models is rarely explored to enhance recommendation performance. ii) Methods based on STKG can introduce external knowledge inconsistent with user preferences, leading to the knowledge noise generated hampering the accuracy of recommendations. To this end, we propose a novel approach called STKG-PLM that integrates <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">STKG</u> contrastive learning and <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">p</u>rompt pre-trained <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">l</u>anguage <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</u>odel (PLM) to enhance the next POI recommendation. Specifically, we design a spatial-temporal trajectory prompt template that transforms structured trajectories into text corpus based on STKG, serving as the input of PLM to understand the movement pattern of users from coarse-grained and fine-grained perspectives. Additionally, we propose an STKG contrastive learning framework to mitigate the introduced knowledge noise. Extensive experiments on three real-world datasets demonstrate that STKG-PLM exhibits notable performance improvements over the state-of-the-art baseline methods.