{"type":"ScholarlyArticle","title":"Content-Aware Hierarchical Point-of-Interest Embedding Model for Successive POI Recommendation","authors":[{"name":"Buru Chang","slug":"buru-chang","given":"Buru","family":"Chang","affiliation":"Korea University"},{"name":"Yonggyu Park","slug":"yonggyu-park","given":"Yonggyu","family":"Park","affiliation":"Korea University"},{"name":"Donghyeon Park","slug":"donghyeon-park","given":"Donghyeon","family":"Park","affiliation":"Korea University"},{"name":"Seongsoon Kim","slug":"seongsoon-kim","given":"Seongsoon","family":"Kim","affiliation":"Naver Corporation"},{"name":"Jaewoo Kang","slug":"jaewoo-kang","given":"Jaewoo","family":"Kang","affiliation":"Korea University"}],"abstract":"Recommending a point-of-interest (POI) a user will visit next based on temporal and spatial context information is an important task in mobile-based applications. Recently, several POI recommendation models based on conventional sequential-data modeling approaches have been proposed. However, such models focus on only a user's check-in sequence information and the physical distance between POIs. Furthermore, they do not utilize the characteristics of POIs or the relationships between POIs. To address this problem, we propose CAPE, the first content-aware POI embedding model which utilizes text content that provides information about the characteristics of a POI. CAPE consists of a check-in context layer and a text content layer. The check-in context layer captures the geographical influence of POIs from the check-in sequence of a user, while the text content layer captures the characteristics of POIs from the text content. To validate the efficacy of CAPE, we constructed a large-scale POI dataset. In the experimental evaluation, we show that the performance of the existing  POI recommendation models can be significantly improved by simply applying CAPE to the models.","doi":"10.24963/ijcai.2018/458","url":"https://ijcai.org/proceedings/2018/458","pdf":"https://ijcai.org/proceedings/2018/0458.pdf","markdown":"https://ijcai.org/proceedings/2018/458.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/458","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning Applications","track":"Main track","number":458,"pages":{"first":3301,"last":3307},"keywords":["Machine Learning: Machine Learning","Machine Learning: Recommender Systems","Machine Learning: Data Mining","Machine Learning: Neural Networks","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Chen et al., 2014] Meng Chen, Yang Liu, and Xiaohui Yu. 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