Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction
This work addresses the problem of improving human mobility prediction for applications like urban planning, but it appears incremental as it builds on existing LLM and MoE techniques.
The paper tackled the problem of next location prediction by addressing limitations in capturing location semantics and modeling user heterogeneity, resulting in a novel framework that achieved superior performance in predictive accuracy, cross-domain generalization, and interpretability across real-world urban datasets.
Next location prediction plays a critical role in understanding human mobility patterns. However, existing approaches face two core limitations: (1) they fall short in capturing the complex, multi-functional semantics of real-world locations; and (2) they lack the capacity to model heterogeneous behavioral dynamics across diverse user groups. To tackle these challenges, we introduce NextLocMoE, a novel framework built upon large language models (LLMs) and structured around a dual-level Mixture-of-Experts (MoE) design. Our architecture comprises two specialized modules: a Location Semantics MoE that operates at the embedding level to encode rich functional semantics of locations, and a Personalized MoE embedded within the Transformer backbone to dynamically adapt to individual user mobility patterns. In addition, we incorporate a history-aware routing mechanism that leverages long-term trajectory data to enhance expert selection and ensure prediction stability. Empirical evaluations across several real-world urban datasets show that NextLocMoE achieves superior performance in terms of predictive accuracy, cross-domain generalization, and interpretability