Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction

TL;DR

NextLocMoE introduces a dual-level Mixture-of-Experts framework leveraging large language models to improve personalized and semantic-aware next location prediction, addressing limitations of existing methods.

Contribution

It presents a novel MoE-based architecture with specialized modules for location semantics and user personalization, enhancing prediction accuracy and interpretability.

Findings

Outperforms existing models in accuracy across multiple datasets

Demonstrates strong cross-domain generalization

Provides interpretable insights into location semantics

Abstract

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