Proceedings

MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation

Huimin Sun, Jiajie Xu, Kai Zheng, Pengpeng Zhao, Pingfu Chao, Xiaofang Zhou

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Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence. Main Track. Pages 3017-3023.

https://doi.org/10.24963/ijcai.2021/415

Keywords

  • Data Mining: Mining Spatial, Temporal Data
  • Machine Learning: Recommender Systems
  • Humans and AI: Personalization and User Modeling

Abstract

Next Point-of-Interest (POI) recommendation is of great value for location-based services. Existing solutions mainly rely on extensive observed data and are brittle to users with few interactions. Unfortunately, the problem of few-shot next POI recommendation has not been well studied yet. In this paper, we propose a novel meta-optimized model MFNP, which can rapidly adapt to users with few check-in records. Towards the cold-start problem, it seamlessly integrates carefully designed user-specific and region-specific tasks in meta-learning, such that region-aware user preferences can be captured via a rational fusion of region-independent personal preferences and region-dependent crowd preferences. In modelling region-dependent crowd preferences, a cluster-based adaptive network is adopted to capture shared preferences from similar users for knowledge transfer. Experimental results on two real-world datasets show that our model outperforms the state-of-the-art methods on next POI recommendation for cold-start users.

Huimin Sun, Jiajie Xu, Kai Zheng, Pengpeng Zhao, Pingfu Chao, and Xiaofang Zhou. 2021. MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation. In Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21. IJCAI, 3017–3023. https://doi.org/10.24963/ijcai.2021/415
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