---
title: "MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation"
authors:
  - "Huimin Sun"
  - "Jiajie Xu"
  - "Kai Zheng"
  - "Pengpeng Zhao"
  - "Pingfu Chao"
  - "Xiaofang Zhou"
doi: "10.24963/ijcai.2021/415"
url: "https://ijcai.org/proceedings/2021/415"
pdf: "https://ijcai.org/proceedings/2021/0415.pdf"
venue: "Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence"
year: 2021
pages: "3017-3023"
track: "Main Track"
keywords:
  - "Data Mining: Mining Spatial, Temporal Data"
  - "Machine Learning: Recommender Systems"
  - "Humans and AI: Personalization and User Modeling"
---

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

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

*Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence*, pages 3017-3023. DOI: [10.24963/ijcai.2021/415](https://doi.org/10.24963/ijcai.2021/415)

## 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.