{"type":"ScholarlyArticle","title":"Learning Hierarchy-Enhanced POI Category Representations Using Disentangled Mobility Sequences","authors":[{"name":"Hongwei Jia","slug":"hongwei-jia","given":"Hongwei","family":"Jia","affiliation":"School of Software , Shandong University"},{"name":"Meng Chen","slug":"meng-chen","given":"Meng","family":"Chen","affiliation":"School of Software , Shandong University"},{"name":"Weiming Huang","slug":"weiming-huang","given":"Weiming","family":"Huang","affiliation":"School of Computer Science and Engineering , Nanyang Technological University"},{"name":"Kai Zhao","slug":"kai-zhao","given":"Kai","family":"Zhao","affiliation":"Robinson College of Business , Georgia State University"},{"name":"Yongshun Gong","slug":"yongshun-gong","given":"Yongshun","family":"Gong","affiliation":"School of Software , Shandong University"}],"abstract":"Points of interest (POIs) carry a wealth of semantic information of varying locations in cities and thus have been widely used to enable various location-based services. To understand POI semantics, existing methods usually model contextual correlations of POI categories in users' check-in sequences and embed categories into a latent space based on the word2vec framework. However, such an approach does not fully capture the underlying hierarchical relationship between POI categories and can hardly integrate the category hierarchy into various deep sequential models. To overcome this shortcoming, we propose a Semantically Disentangled POI Category Embedding Model (SD-CEM) to generate hierarchy-enhanced category representations using disentangled mobility sequences. Specifically, first, we construct disentangled mobility sequences using human mobility data based on the semantics of POIs. Then we utilize the POI category hierarchy to initialize a hierarchy-enhanced representation for each category in the disentangled sequences, employing an attention mechanism. Finally, we optimize these category representations by incorporating both the masked category prediction task and the next category prediction task. To evaluate the effectiveness of SD-CEM, we conduct comprehensive experiments using two check-in datasets covering three tasks. Experimental results demonstrate that SD-CEM outperforms several competitive baselines, highlighting its substantial improvement in performance as well as the understanding of learned category representations.","doi":"10.24963/ijcai.2024/231","url":"https://ijcai.org/proceedings/2024/231","pdf":"https://ijcai.org/proceedings/2024/0231.pdf","markdown":"https://ijcai.org/proceedings/2024/231.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/231","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Data Mining","track":"Main Track","number":231,"pages":{"first":2090,"last":2098},"keywords":["Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Bai et al., 2023] Lubin Bai, Weiming Huang, Xiuyuan Zhang, Shihong Du, Gao Cong, Haoyu Wang, and Bo Liu. 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