{"type":"ScholarlyArticle","title":"Next Point-of-Interest Recommendation with Inferring Multi-step Future Preferences","authors":[{"name":"Lu Zhang","slug":"lu-zhang","given":"Lu","family":"Zhang","affiliation":"Nanyang Technological University , Singapore"},{"name":"Zhu Sun","slug":"zhu-sun","given":"Zhu","family":"Sun","affiliation":"* STAR Institute of High Performance Computing , Singapore; * STAR Centre for Frontier AI Research , Singapore"},{"name":"Ziqing Wu","slug":"ziqing-wu","given":"Ziqing","family":"Wu","affiliation":"Nanyang Technological University , Singapore"},{"name":"Jie Zhang","slug":"jie-zhang","given":"Jie","family":"Zhang","affiliation":"Nanyang Technological University , Singapore"},{"name":"Yew Soon Ong","slug":"yew-soon-ong","given":"Yew-Soon","family":"Ong","affiliation":"Nanyang Technological University , Singapore"},{"name":"Xinghua Qu","slug":"xinghua-qu","given":"Xinghua","family":"Qu","affiliation":"Bytedance AI Lab , Singapore"}],"abstract":"Existing studies on next point-of-interest (POI) recommendation mainly attempt to learn user preference from the past and current sequential behaviors. They, however, completely ignore the impact of future behaviors on the decision-making, thus hindering the quality of user preference learning. Intuitively, users' next POI visits may also be affected by their multi-step future behaviors, as users may often have activity planning in mind. To fill this gap, we propose a novel Context-aware Future Preference inference Recommender (CFPRec) to help infer user future preference in a self-ensembling manner. In particular, it delicately derives multi-step future preferences from the learned past preference thanks to the periodic property of users' daily check-ins, so as to implicitly mimic user’s activity planning before her next visit. The inferred future preferences are then seamlessly integrated with the current preference for more expressive user preference learning. Extensive experiments on three datasets demonstrate the superiority of CFPRec against state-of-the-arts.","doi":"10.24963/ijcai.2022/521","url":"https://ijcai.org/proceedings/2022/521","pdf":"https://ijcai.org/proceedings/2022/0521.pdf","markdown":"https://ijcai.org/proceedings/2022/521.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/521","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Machine Learning","track":"Main Track","number":521,"pages":{"first":3751,"last":3757},"keywords":["Humans and AI: Personalization and User Modeling","Machine Learning: Recommender Systems"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986869","length_sec":87,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38985012","length_sec":326,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Chen et al., 2021] Yudong Chen, Xin Wang, Miao Fan, Jizhou Huang, Shengwen Yang, and Wenwu Zhu. 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