用专家混合模型提升位置预测的个性化与语义理解能力
Mixture-of-Experts for Personalized and Semantic-Aware Next Location Prediction
- 双层专家混合架构：分别处理地点语义和用户个性化行为
- 在多个城市数据集上准确率超越现有方法，跨域泛化能力强
- 适合关注轨迹预测、个性化推荐的研究者与应用开发者
下个位置预测在理解人类移动模式中至关重要。然而，现有方法存在两大局限：(1) 难以捕捉真实世界位置的复杂多维语义；(2) 缺乏对不同用户群体异质性行为动态的建模能力。为此，我们提出NextLocMoE，一种基于大语言模型并采用双层专家混合（MoE）设计的新框架。其包含两个专用模块：位于嵌入层的位置语义MoE，用于编码地点的丰富功能语义；嵌入Transformer主干中的个性化MoE，可动态适应个体出行模式。此外，引入历史感知路由机制，利用长期轨迹数据优化专家选择，保障预测稳定性。在多个真实城市数据集上的实证评估表明，NextLocMoE在预测准确率、跨域泛化能力和可解释性方面均表现卓越。
原文摘要 · Abstract (English)
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
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