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Current study on next POI recommendation mainly explores user sequentialtransitions with the fine-grained individual-user POI check-in trajectoriesonly, which suffers from the severe check-in data sparsity issue. In fact,coarse-grained signals (i.e., region- and global-level check-ins) in suchsparse check-ins would also benefit to augment user preference learning.Specifically, our data analysis unveils that user movement exhibits noticeablepatterns w.r.t. the regions of visited POIs. Meanwhile, the global all-usercheck-ins can help reflect sequential regularities shared by the crowd. We are,therefore, inspired to propose the MCMG: a Multi-Channel next POIrecommendation framework with Multi-Granularity signals categorized from twoorthogonal perspectives, i.e., fine-coarse grained check-ins at eitherPOI/region level or local/global level. Being equipped with three modules(i.e., global user behavior encoder, local multi-channel encoder, andregion-aware weighting strategy), MCMG is capable of capturing both fine- andcoarse-grained sequential regularities as well as exploring the dynamic impactof multi-channel by differentiating the region check-in patterns. Extensiveexperiments on four real-world datasets show that our MCMG significantlyoutperforms state-of-the-art next POI recommendation approaches.

A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals