论文标题
多渠道的下一个POI推荐框架，带有多粒度入住信号
A Multi-Channel Next POI Recommendation Framework with Multi-Granularity Check-in Signals
论文作者
论文摘要
关于下一个POI建议的当前研究主要探索用户顺序的过渡，仅使用细颗粒的单个用户POI登记轨迹，这遭受了严重的入住数据稀疏问题。实际上，在这种稀疏的检查中，粗粒粒度的信号（即区域和全球级数检查）也将受益于增强用户偏好学习。具体而言，我们的数据分析揭示了用户运动表现出明显模式W.R.T.访问的pois地区。同时，全球全用户签到可以有助于反映人群共享的顺序规律性。因此，我们受到启发提出MCMG：一个多渠道下一个POI推荐框架，该框架具有从两个正交角度分类的多粒性信号，即在POI/区域级别或本地/全球级别上，在POI/区域级别上进行了细砂粒度检查。 MCMG配备了三个模块（即全球用户行为编码器，本地多渠道编码器和区域感知的加权策略），MCMG能够通过区分区域签到模式来捕获细粒和粗粒顺序的正规定期和粗粒的顺序规律，并探索多渠道的动态影响。在四个现实世界数据集上进行的广泛实验表明，我们的MCMG显着优于最先进的下一个POI推荐方法。
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.
