LLM-Driven Multi-Perspective Location Completion for Next Location Prediction

摘要

Next location prediction aims to infer the next location users are likely to visit based on their historical check-in data. However, existing methods assume that check-in data is complete, overlooking the subjective nature of users’ check-in behavior, leading to inaccurate capture of user preferences. Recently, Large Language Models (LLMs) have offered a promising approach to location completion due to their extensive world knowledge. Nevertheless, our experiments reveal that LLMs struggle to interpret raw geographic coordinate information. To address these challenges, we propose LaMDA, an LLM-driven Multi-perspective Data Augmentation framework that employs dual completion agents to complement user mobility behaviors. Driven by our empirical findings that natural language descriptions align more closely with real-world geographic logic, LaMDA translates geographic coordinates into text to enhance spatial reasoning. Leveraging these semantic descriptions, LaMDA constructs dual agents to complement user mobility: "Micro-Level Completion" fills short-term omissions, while "Macro-Level Completion" infers unrecorded locations based on periodic preferences. Reliability is ensured through tailored real-world point-of-interest (POI) pools and a self-verification mechanism. Finally, a collaborative dual-graph module leverages this augmented data for fine-grained preference modeling. Extensive experiments on three real-world datasets demonstrate that LaMDA significantly outperforms state-of-the-art methods.