{"type":"ScholarlyArticle","title":"Discovering Subsequence Patterns for Next POI Recommendation","authors":[{"name":"Kangzhi Zhao","slug":"kangzhi-zhao","given":"Kangzhi","family":"Zhao","affiliation":"Department of Computer Science and Technology , Tsinghua University"},{"name":"Yong Zhang","slug":"yong-zhang","given":"Yong","family":"Zhang","affiliation":"Department of Computer Science and Technology , Tsinghua University"},{"name":"Hongzhi Yin","slug":"hongzhi-yin","given":"Hongzhi","family":"Yin","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland"},{"name":"Jin Wang","slug":"jin-wang","given":"Jin","family":"Wang","affiliation":"Computer Science Department , University of California"},{"name":"Kai Zheng","slug":"kai-zheng","given":"Kai","family":"Zheng","affiliation":"University of Electronic Science and Technology of China"},{"name":"Xiaofang Zhou","slug":"xiaofang-zhou","given":"Xiaofang","family":"Zhou","affiliation":"School of Information Technology and Electrical Engineering , The University of Queensland"},{"name":"Chunxiao Xing","slug":"chunxiao-xing","given":"Chunxiao","family":"Xing","affiliation":"Department of Computer Science and Technology , Tsinghua University"}],"abstract":"Next Point-of-Interest (POI) recommendation plays an important role in location-based services. State-of-the-art methods learn the POI-level sequential patterns in the user's check-in sequence but ignore the subsequence patterns that often represent the socio-economic activities or coherence of preference of the users. However, it is challenging to integrate the semantic subsequences due to the difficulty to predefine the granularity of the complex but meaningful subsequences. In this paper, we propose Adaptive Sequence Partitioner with Power-law Attention (ASPPA) to automatically identify each semantic subsequence of POIs and discover their sequential patterns. Our model adopts a state-based stacked recurrent neural network to hierarchically learn the latent structures of the user's check-in sequence. We also design a power-law attention mechanism to integrate the domain knowledge in spatial and temporal contexts. Extensive experiments on two real-world datasets demonstrate the effectiveness of our model.","doi":"10.24963/ijcai.2020/445","url":"https://ijcai.org/proceedings/2020/445","pdf":"https://ijcai.org/proceedings/2020/0445.pdf","markdown":"https://ijcai.org/proceedings/2020/445.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/445","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":445,"pages":{"first":3216,"last":3222},"keywords":["Data Mining: Mining Spatial, Temporal Data","Machine Learning: Recommender Systems"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24601/file_24601.m3u8","length_sec":218,"provider":"hls"}],"references":[{"position":1,"raw":"[Blei et al., 2003] David M. 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