Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
139 tokens/sec
GPT-4o
47 tokens/sec
Gemini 2.5 Pro Pro
43 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation (2312.15717v1)

Published 25 Dec 2023 in cs.AI, cs.CY, and cs.LG

Abstract: In the realm of human mobility, the decision-making process for selecting the next-visit location is intricately influenced by a trade-off between spatial and temporal constraints, which are reflective of individual needs and preferences. This trade-off, however, varies across individuals, making the modeling of these spatial-temporal dynamics a formidable challenge. To address the problem, in this work, we introduce the "Spatial-temporal Induced Hierarchical Reinforcement Learning" (STI-HRL) framework, for capturing the interplay between spatial and temporal factors in human mobility decision-making. Specifically, STI-HRL employs a two-tiered decision-making process: the low-level focuses on disentangling spatial and temporal preferences using dedicated agents, while the high-level integrates these considerations to finalize the decision. To complement the hierarchical decision setting, we construct a hypergraph to organize historical data, encapsulating the multi-aspect semantics of human mobility. We propose a cross-channel hypergraph embedding module to learn the representations as the states to facilitate the decision-making cycle. Our extensive experiments on two real-world datasets validate the superiority of STI-HRL over state-of-the-art methods in predicting users' next visits across various performance metrics.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (38)
  1. Optimality and approximation with policy gradient methods in markov decision processes. In Conference on Learning Theory, 64–66.
  2. Human mobility trace acquisition and social interactions monitoring for business intelligence using smartphones. In 2012 16th Panhellenic Conference on Informatics, 1–6.
  3. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 135–146.
  4. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 273–297.
  5. Adaptive Path-Memory Network for Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2304.12604.
  6. Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning. arXiv preprint arXiv:2309.03251.
  7. Denoising-oriented deep hierarchical reinforcement learning for next-basket recommendation. In ICASSP 2022 IEEE International Conference on Acoustics, Speech and Signal Processing, 4093–4097.
  8. Deepmove: Predicting human mobility with attentional recurrent networks. In Proceedings of the 2018 World Wide Web Conference, 1459–1468.
  9. Reinforced explainable knowledge concept recommendation in MOOCs. ACM Transactions on Intelligent Systems and Technology, 1–20.
  10. Multi-view MOOC quality evaluation via information-aware graph representation learning. In Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 8070–8077.
  11. HST-LSTM: A hierarchical spatial-temporal long-short term memory network for location prediction. In Lang, J., ed., Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2341–2347.
  12. Geography-aware sequential location recommendation. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2009–2019.
  13. Hypergraph regularized low-rank tensor multi-view subspace clustering via L1 norm constraint. Applied Intelligence, 1–18.
  14. Predicting the next location: a recurrent model with spatial and temporal contexts. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, 194–200.
  15. STAN: Spatio-temporal attention network for next location recommendation. In Leskovec, J.; Grobelnik, M.; Najork, M.; Tang, J.; and Zia, L., eds., Proceedings of the 2021 World Wide Web Conference, 2177–2185.
  16. Human-level control through deep reinforcement learning. Nature, 529–533.
  17. Equitable healthcare provision: uncovering the impact of the mobility effect on human development. Information Systems Management, 2–20.
  18. Factorizing personalized markov chains for next-basket recommendation. In Proceedings of the 2010 World Wide Web Conference, 811–820.
  19. A simple multi-armed nearest-neighbor bandit for interactive recommendation. In Proceedings of the 13th ACM Conference on Recommender Systems, 358–362.
  20. Where to go next: Modeling long- and short-term user preferences for point-of-interest recommendation. In Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 214–221.
  21. Reimagining city configuration: Automated urban planning via adversarial learning. In Proceedings of the 28th International Conference on Advances in Geographic Information Systems, 497–506.
  22. Reinforced imitative graph learning for mobile user profiling. IEEE Transactions on Knowledge and Data Engineering, 12944–12957.
  23. Joint charging and relocation recommendation for E-taxi drivers via multi-agent mean field hierarchical reinforcement learning. IEEE Transactions on Mobile Computing, 1274–1290.
  24. Adversarial substructured representation learning for mobile user profiling. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 130–138.
  25. Learning urban community structures: A collective embedding perspective with periodic spatial-temporal mobility graphs. ACM Transactions on Intelligent Systems and Technology, 1–28.
  26. Exploiting mutual information for substructure-aware graph representation learning. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 3415–3421.
  27. Incremental mobile user profiling: Reinforcement learning with spatial knowledge graph for modeling event streams. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 853–861.
  28. Next point-of-interest recommendation on resource-constrained mobile devices. In Huang, Y.; King, I.; Liu, T.; and van Steen, M., eds., Proceedings of the 2020 World Wide Web Conference, 906–916.
  29. Learning graph-based disentangled representations for next POI recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1154–1163.
  30. Hierarchical reinforcement learning for integrated recommendation. In Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 4521–4528.
  31. Spatio-temporal hypergraph learning for next POI recommendation. In Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, 403–412.
  32. Location prediction over sparse user mobility traces using rnns. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, 2184–2190.
  33. Revisiting user mobility and social relationships in LBSNs: a hypergraph embedding approach. In Proceedings of the 2019 World Wide Web Conference, 2147–2157.
  34. Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 129–142.
  35. GETNext: Trajectory flow map enhanced transformer for next POI recommendation. In Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1144–1153.
  36. Expanrl: Hierarchical reinforcement learning for course concept expansion in MOOCs. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, 770–780.
  37. Hierarchical reinforcement learning for course recommendation in MOOCs. In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 435–442.
  38. Where to go next: A spatio-temporal gated network for next poi recommendation. IEEE Transactions on Knowledge and Data Engineering, 2512–2524.
Citations (6)
View on Semantic Scholar

Summary

  • The paper introduces a two-layer HRL framework that decouples spatial and temporal dynamics for precise mobility predictions.
  • It leverages hypergraph representation to model multi-faceted data including POI, zones, and time channels, outperforming eight baseline methods.
  • Extensive experiments on New York and Tokyo datasets show significant gains in Recall, F1, MRR, and NDCG metrics.

Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation

Human mobility modeling is a complex but critical problem, touching on various domains from urban planning and transportation to public health. In the paper titled "Spatial-Temporal Interplay in Human Mobility: A Hierarchical Reinforcement Learning Approach with Hypergraph Representation," the authors present a novel framework, STI-HRL (Spatial-temporal Induced Hierarchical Reinforcement Learning), to address this challenge by integrating advanced hierarchical reinforcement learning methods with hypergraph-based data representation.

Key Contributions

The paper offers several significant contributions:

  1. Hierarchical Reinforcement Learning Framework: The authors propose a two-tiered decision-making process comprising a low-level that decouples spatial and temporal factors using dedicated agents and a high-level that integrates their insights to make final mobility decisions.
  2. Hypergraph Representation: To encapsulate the intricate dynamics of human mobility, a hypergraph is constructed, organizing historical data while preserving multi-aspect semantics, including POI (Point of Interest), zone, and time channels.
  3. Cross-Channel Hypergraph Embedding Module: This innovative module effectively learns representations of states, which are critical for facilitating the decision-making process within the STI-HRL framework.
  4. Extensive Empirical Validation: The framework's effectiveness is validated through extensive experiments on two real-world datasets (New York and Tokyo), consistently outperforming state-of-the-art methods across various metrics.

Detailed Insights

Hierarchical Reinforcement Learning (HRL)

The HRL framework used here features a two-layer model:

  • Low-Level Agents: These agents are subdivided into spatial and temporal agents to disentangle the complexities of spatial-temporal preferences. The spatial agent focuses on geographic factors, while the temporal agent emphasizes temporal dynamics.
  • High-Level Agent: Integrating the insights from low-level agents, this agent aims to amalgamate spatial and temporal considerations to produce well-rounded mobility decisions.

Hypergraph Construction

A crucial aspect of this paper is the use of a hypergraph to model the multitude of human mobility records. The authors define four types of vertices corresponding to POI, POI category, zone, and time channels, and four types of hyperedges: POI hyperedge, zone hyperedge, time hyperedge, and event hyperedge.

The hyperedges capture different aspects of mobility data, which are then used by the HRL framework to predict future movements accurately.

Experimental Evaluation

The empirical studies are conducted on well-known datasets and benchmark the STI-HRL framework against eight baseline models, which include recent deep learning-based methods like ST-RNN, DeepMove, and LSTPM. The results demonstrate superior performance across all evaluation metrics, including Recall, F1, MRR, and NDCG.

In particular, in the New York dataset, STI-HRL surpasses the next best method by more than:

  • 5.67% in Recall @5
  • 8.90% in F1 @5
  • 8.60% in MRR @5
  • 6.56% in NDCG @5

Similarly, in the Tokyo dataset:

  • 7.30% in Recall @5
  • 13.10% in F1 @5
  • 7.03% in MRR @5
  • 6.53% in NDCG @5

Implications and Future Directions

The significant performance improvements validate the authors' hypothesis that capturing the spatial-temporal interplay is essential for accurate human mobility modeling. The hypergraph-based representation additively enhances this by preserving multi-faceted semantics in the data.

Theoretically, the work advances the understanding of spatial-temporal dynamics and their influence on human mobility. Practically, it can be extended to various real-world applications, such as optimizing urban infrastructure, enhancing public transportation systems, and improving the efficacy of location-based services.

Future research might focus on:

  1. Scalability: Adapting the framework to handle larger and more diverse datasets, including those from different geographical locations.
  2. Adaptability: Making the HRL framework adaptable to different urban contexts and mobility patterns.
  3. Real-time Predictions: Extending the framework to enable real-time predictions and dynamic adjustments based on live data inputs.
  4. Privacy Concerns: Addressing issues related to data privacy in the context of human mobility.

Conclusion

The STI-HRL framework represents a substantial step forward in the field of human mobility modeling. By leveraging a hierarchical reinforcement learning approach complemented with a comprehensive hypergraph representation, the authors have managed to capture the multifaceted spatial-temporal dynamics inherent in human decision-making processes. This work not only outperforms existing methodologies but also opens up pathways for future research and practical applications in the domain of human mobility.

File Document Download Save Streamline Icon: https://streamlinehq.com PDF File Document Download Save Streamline Icon: https://streamlinehq.com Markdown
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets