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.

Prompt tuning (PT) offers a cost-effective alternative to fine-tuning large-scale pre-trained language models (PLMs), requiring only a few parameters in soft prompt tokens added before the input text. However, existing PT approaches face two significant issues: i They overlook intrinsic semantic associations between soft prompt tokens, leading to high discreteness and limited interactions, thus reducing the model’s comprehension and effectiveness in complex tasks. ii Due to the complexity of downstream tasks, long soft prompt is necessitated to improve performance, but prompt length correlates positively with memory usage and computational costs. Achieving high efficiency and performance remains an ongoing challenge. To address these issues, we propose a novel Low-parameters Prompt Tuning (LAMP) method, which leverages prompt decomposition and compressed outer product. Specifically, the prompt decomposition module employs Truncated SVD to reduce training parameters and significantly lower the dimensionality of the soft prompt parameter space. It then utilizes a compressed outer product module to facilitate multiple interactions among prompt tokens, exploring their intrinsic associations to enhance knowledge representation. Finally, LAMP uses average pooling to reduce memory usage and training/inference time. Extensive experiments across six architectures and eight datasets demonstrate that LAMP outperforms state-of-the-art PT-based and LoRA-based methods in performance and efficiency.