mlsanityData to 14 Oct 2026

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

arXiv 2505.24597 ↗First posted 30 May 2025cs.AIcited by 2

On the map: Mixture-of-experts → MoE for time series and graphs

Abstract (arXiv)

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

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in MoE for time series and graphs, one of 12 directions in Mixture-of-experts. Builds on 34 of its 65 references that are in the map. First posted on arXiv 30 May 2025.

Builds on

34 of 65 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Graph Mixture of Experts and Memory-augmented Routers for Multivariate Time Series Anomaly Detection26 Dec
  2. 2024Moirai-MoE: Empowering Time Series Foundation Models with Sparse Mixture of Experts14 Oct
  3. 2024Time-MoE: Billion-Scale Time Series Foundation Models with Mixture of Experts24 Sep

Other references · 31

  1. 2017Attention Is All You Needcited by 60,361
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 14,132
  4. 2021Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsitycited by 3,073
  5. 2014Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modelingcited by 2,367
  6. 2020GShard: Scaling Giant Models with Conditional Computation and Automatic Shardingcited by 1,631
  7. 2024DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Modelscited by 867
  8. 2016Temporal Convolutional Networks for Action Segmentation and Detectioncited by 508
  9. 2018Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Networkcited by 420
  10. 2025Layer by Layer: Uncovering Hidden Representations in Language Modelscited by 311
  11. 2024Not All Experts are Equal: Efficient Expert Pruning and Skipping for Mixture-of-Experts Large Language Modelscited by 146
  12. 2024LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-trainingcited by 123
19 more
  1. 2021STAN: Spatio-Temporal Attention Network for Next Location Recommendationcited by 102
  2. 2024Large Language Models as Urban Residents: An LLM Agent Framework for Personal Mobility Generationcited by 100
  3. 2023Where Would I Go Next? Large Language Models as Human Mobility Predictorscited by 93
  4. 2024Harder Tasks Need More Experts: Dynamic Routing in MoE Modelscited by 72
  5. 2023GETNext: Trajectory Flow Map Enhanced Transformer for Next POI Recommendationcited by 58
  6. 2024A Closer Look into Mixture-of-Experts in Large Language Modelscited by 48
  7. 2024Large Language Models for Next Point-of-Interest Recommendationcited by 40
  8. 2024Multimodal Instruction Tuning with Conditional Mixture of LoRAcited by 36
  9. 2024AgentMove: A Large Language Model based Agentic Framework for Zero-shot Next Location Predictioncited by 25
  10. 2024Large Language Models are Zero-Shot Next Location Predictorscited by 22
  11. 2022Context-aware multi-head self-attentional neural network model for next location predictioncited by 21
  12. 2024MoE Jetpack: From Dense Checkpoints to Adaptive Mixture of Experts for Vision Taskscited by 9
  13. 2024PMoE: Progressive Mixture of Experts with Asymmetric Transformer for Continual Learningcited by 8
  14. 2024Taming the Long Tail in Human Mobility Predictioncited by 8
  15. 2024Llama-Mob: Instruction-Tuning Llama-3-8B Excels in City-Scale Mobility Predictioncited by 6
  16. 2024CausalMob: Causal Human Mobility Prediction with LLMs-derived Human Intentions toward Public Eventscited by 5
  17. 2021Multiwave COVID-19 Prediction from Social Awareness using Web Search and Mobility Datacited by 3
  18. 2021SanMove: Next Location Recommendation via Self-Attention Networkcited by 2
  19. 2025SEAGET: Seasonal and Active hours guided Graph Enhanced Transformer for the next POI recommendationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 1.

2 papers.

  1. 2025Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectivescited by 10
  2. 2026Bus-Conditioned Zero-Shot Trajectory Generation via Task Arithmeticcited by 2

Similar, not cited

Close in wording, no citation either way

  1. 2025MoE-TransMov: A Transformer-based Model for Next POI Prediction in Familiar & Unfamiliar Movements
  2. 2025MoveGPT: Scaling Mobility Foundation Models with Spatially-Aware Mixture of Experts
  3. 2024ST-MoE-BERT: A Spatial-Temporal Mixture-of-Experts Framework for Long-Term Cross-City Mobility Prediction
  4. 2026Enhancing Human Mobility Prediction with Spatially Aware LLM-based Multi-Agent Systems
  5. 2025Adaptive Location Hierarchy Learning for Long-Tailed Mobility Prediction
  6. 2025SeMob: Semantic Synthesis for Dynamic Urban Mobility Prediction
  7. 2026ARMove: Learning to Predict Human Mobility through Agentic Reasoning
  8. 2025Enhancing Large Language Models for Mobility Analytics with Semantic Location Tokenization