{"type":"ScholarlyArticle","title":"MFNP: A Meta-optimized Model for Few-shot Next POI Recommendation","authors":[{"name":"Huimin Sun","slug":"huimin-sun","given":"Huimin","family":"Sun","affiliation":""},{"name":"Jiajie Xu","slug":"jiajie-xu","given":"Jiajie","family":"Xu","affiliation":""},{"name":"Kai Zheng","slug":"kai-zheng","given":"Kai","family":"Zheng","affiliation":""},{"name":"Pengpeng Zhao","slug":"pengpeng-zhao","given":"Pengpeng","family":"Zhao","affiliation":""},{"name":"Pingfu Chao","slug":"pingfu-chao","given":"Pingfu","family":"Chao","affiliation":""},{"name":"Xiaofang Zhou","slug":"xiaofang-zhou","given":"Xiaofang","family":"Zhou","affiliation":""}],"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.","doi":"10.24963/ijcai.2021/415","url":"https://ijcai.org/proceedings/2021/415","pdf":"https://ijcai.org/proceedings/2021/0415.pdf","markdown":"https://ijcai.org/proceedings/2021/415.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/415","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":415,"pages":{"first":3017,"last":3023},"keywords":["Data Mining: Mining Spatial, Temporal Data","Machine Learning: Recommender Systems","Humans and AI: Personalization and User Modeling"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}