5 papers
An Empirical Evaluation of Cross-City POI Recommendation on a Large-Scale Benchmark
Peibo Li, Yang Song, Hao Xue +2
Cross-city point-of-interest (POI) recommendation is crucial for navigating unfamiliar urban environments, yet its progress has historically been constrained by data limitations. U…
HiT-JEPA: A Hierarchical Self-supervised Trajectory Embedding Framework for Similarity Computation
Lihuan Li, Hao Xue, Shuang Ao +2
The representation of urban trajectory data plays a critical role in effectively analyzing spatial movement patterns. Despite considerable progress, the challenge of designing traj…
Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation
Peibo Li, Shuang Ao, Hao Xue +5
Advancing large language models (LLMs) for the next point-of-interest (POI) recommendation task faces two fundamental challenges: (i) although existing methods produce semantic IDs…
T-JEPA: A Joint-Embedding Predictive Architecture for Trajectory Similarity Computation
Lihuan Li, Hao Xue, Yang Song +1
Trajectory similarity computation is an essential technique for analyzing moving patterns of spatial data across various applications such as traffic management, wildlife tracking,…
Large Language Models for Next Point-of-Interest Recommendation
Peibo Li, Maarten de Rijke, Hao Xue +3
The next Point of Interest (POI) recommendation task is to predict users' immediate next POI visit given their historical data. Location-Based Social Network (LBSN) data, which is…