Modeling hierarchical category transition for next POI recommendation with uncertain check-ins

• Published in: Information sciences Vol. 515; pp. 169 - 190
• Main Authors: Zhang, Lu, Sun, Zhu, Zhang, Jie, Kloeden, Horst, Klanner, Felix
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.04.2020
• Subjects: Category hierarchy; Category transition; Collective POI; Next POI recommendation; Representation learning; Uncertain check-in; Category transition; Representation learning; Collective POI; Uncertain check-in; Category hierarchy; Next POI recommendation
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2019.12.006

•A new research problem of predicting next individual POIs with uncertain check-ins at collective POIs.•A novel framework exploits hierarchical category transition to model users’ preference transitions in different granularity.•Three newly-constructed POI datasets that are suitable for our proposed research problem.•Extensive experiments on the newly-constructed datasets to demonstrate the superiority of the proposed framework against state-of-the-arts. Most existing next POI recommendation studies rely on users’ certain check-ins at individual POIs (e.g., Italian restaurant). In reality, users may leave some uncertain check-ins in the places (e.g., shopping mall), which are named as collective POIs. It indicates that we cannot always access users’ precise check-ins at collective POIs, thus existing approaches fail to work well. To this end, we propose a new research problem, that aims to recommend next individual POIs with uncertain check-ins at collective POIs. It is, however, difficult to learn complete and accurate users’ check-in transition patterns with uncertain check-ins. Besides, uncertain check-ins aggravate the cold start issue, as the individual POIs inside collective POIs cannot be observed in users’ historical check-ins. To tackle these challenges, we devise a novel hierarchical category transition (HCT) framework, which exploits category transitions at different layers to model users’ preference transition patterns in different granularity. By doing so, HCT predicts users’ preferred categories inside collective POIs. As bounded to specific categories, HCT further adopts hierarchical dependencies between POIs and categories to capture the semantic relatedness of POIs, thus easing the cold start issue. Empirical studies on multiple datasets show the superiority of HCT against state-of-the-art algorithms.