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

TL;DR

This paper introduces MCMG, a multi-channel framework that leverages multi-granularity check-in signals, including coarse and fine levels, to improve next POI recommendation accuracy amidst data sparsity.

Contribution

The paper proposes a novel multi-channel framework that integrates multi-granularity signals from different perspectives to enhance POI recommendation performance.

Findings

MCMG significantly outperforms existing methods in four real-world datasets.

Multi-granularity signals effectively address data sparsity issues.

Region-aware weighting improves the capture of dynamic user preferences.

Abstract

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