Hierarchical and Multi-Resolution Preference Modeling for Next POI Recommendation

Next Point-Of-Interest (POI) recommendation has attracted extensive attention recently, benefiting from the large volumes of check-in records on location-based social networks (LBSNs). Most of the existing works either consider user's long- and short-term preferences or only one of them, ignoring users' preferences have multiple different resolutions. Moreover, these methods suffer from the high sparsity issue. Although hierarchical category information contributes to alleviating this problem, it has not been fully exploited and integrated with spatiotemporal contexts in next POI recommendation. To this end, we propose a novel method named Hierarchical and Multi-Resolution Preference Modeling (HMRPM), which simultaneously models hierarchical personalized preferences at three different resolutions, i.e., we learn long-term, short-term and transient preferences. HMRPM consists of three corresponding preference modeling modules: (1) The long-term module captures general preferences of users by modeling user-POI interactions and hierarchical category-level interactions. (2) The short-term module utilizes a hierarchical category-aware self-attention mechanism to capture users' recent preferences. Spatiotemporal position embeddings and user-aware attention are also proposed. (3) The transient module captures hierarchical current preferences by modeling hierarchical transient transition patterns. Extensive experimental results on two public datasets demonstrate the proposed method significantly outperforms other state-of-the-art methods.