Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation

Rao, Xuan; Shang, Shuo; Chen, Lisi; Jiang, Renhe; Han, Peng

doi:10.24963/ijcai.2025/856

Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation

Xuan Rao, Shuo Shang, Lisi Chen, Renhe Jiang, Peng Han

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 7697-7705.

https://doi.org/10.24963/ijcai.2025/856

Keywords

Multidisciplinary Topics and Applications: MTA: Transportation

Abstract

Next POInt-of-Interest (POI) recommendation predicts a user's next move and facilitates location-based services such as navigation and travel planning. SOTA methods fuse each POI and its contexts (e.g., time, category, and region) into a single representation to model sequential user movement. This hinders the effective utilization of context information, and diverse user preferences are also neglected. To tackle these limitations, we propose Disentangled and Personalized Representation Learning (DPRL) as a novel method for next POI recommendation. DPRL decouples POIs and contexts during representation learning, capturing their sequential regularities independently using separate recurrent neural networks (RNNs). To model the preference of each user, DPRL adopts an aggregation mechanism that integrates dynamic user preferences and spatial-temporal factors into the learned representations. We compare DPRL with 16 state-of-the-art baselines. The results show that DPRL outperforms all baselines and achieves an average accuracy improvement of 10.53% over the best-performing baseline.

Xuan Rao, Shuo Shang, Lisi Chen, Renhe Jiang, and Peng Han. 2025. Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 7697–7705. https://doi.org/10.24963/ijcai.2025/856

@inproceedings{ijcai2025p856,
  title     = {Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation},
  author    = {Rao, Xuan and Shang, Shuo and Chen, Lisi and Jiang, Renhe and Han, Peng},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {7697--7705},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/856},
  url       = {https://doi.org/10.24963/ijcai.2025/856},
}

TY  - CPAPER
TI  - Disentangled and Personalized Representation Learning for Next Point-of-Interest Recommendation
AU  - Rao, Xuan
AU  - Shang, Shuo
AU  - Chen, Lisi
AU  - Jiang, Renhe
AU  - Han, Peng
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 7697
EP  - 7705
DO  - 10.24963/ijcai.2025/856
UR  - https://doi.org/10.24963/ijcai.2025/856
ER  -