Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction (2505.24597v1)

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 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

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

The paper under discussion presents a sophisticated framework, NextLocMoE, designed to tackle the inherent challenges of next location prediction. This task, critical for understanding human mobility patterns, faces two predominant challenges: the inability to capture the complex semantics of real-world locations and the inadequacy in accounting for diverse user behavioral dynamics.

NextLocMoE innovatively integrates LLMs with a dual-level Mixture-of-Experts (MoE) architecture to address these complexities. The framework comprises two main modules: a Location Semantics MoE, which operates at the embedding level to capture and encode the rich, multifaceted semantics of locations, and a Personalized MoE, which is embedded within the Transformer backbone to dynamically adapt to individual user mobility patterns.

Experimental Validation

The empirical evaluation of NextLocMoE is conducted on various real-world urban datasets, providing a robust testbed to validate the framework's efficacy. The results are compelling, indicating significant improvements in prediction accuracy, cross-domain generalization, and model interpretability compared to existing methods. Particularly, NextLocMoE demonstrates its superiority in fully-supervised, zero-shot, and cross-city prediction settings, underlining its robustness and transferability.

Methodology Highlights

1. Location Semantics MoE: This module enhances the location embeddings by incorporating multiple function-specific experts, each capturing different semantic roles of a location. This is essential in urban environments where locations often serve multipurpose roles, such as shopping malls, which can simultaneously function as social, commercial, and recreational spaces.
2. Personalized MoE: Within specific LLM layers, this module adapts to user-specific behavioral patterns through specialized experts. This adaptation process is guided by pre-defined user groups, with dynamic expert activation strategies that balance computational efficiency with personalization depth.
3. History-aware Routing Mechanism: To overcome the common challenge of instability in expert selection, NextLocMoE introduces a routing mechanism that integrates long-term historical trajectory data, ensuring more stable and contextually-aware expert activation.

Implications and Future Directions

The implications of this research are multifaceted, both in terms of practical applications and theoretical advancements. Practically, the improved accuracy and interpretability of NextLocMoE hold promise for enhancing a wide range of location-based services, from intelligent transportation systems to personalized urban management solutions.

Theoretically, the integration of MoE with LLMs in a framework like NextLocMoE raises interesting possibilities for future AI developments. The approach of coupling semantic understanding with personalized modeling in predictive tasks could be extended to other domains demanding nuanced context and user adaptations.

Future research could focus on addressing the model's computational demands, particularly regarding memory costs associated with expert networks. Strategies involving expert compression or more efficient parameter sharing could be explored to refine and optimize the deployment of such sophisticated models on a broader scale.

In conclusion, NextLocMoE represents a significant step forward in next location prediction, successfully addressing critical challenges through its innovative dual-MoE architecture. As a result, it enhances both the efficacy and efficiency of predictive modeling in human mobility, setting a new benchmark for future research in this domain.