With　the　development　of　mobile　networks　and　the　rapid　prevalence　of　location-based　social　networks　(LBSN),　a　massive　volume　of　spatiotemporal　data　has　been　generated,　which　is　valuable　for　points　of　interest　(POI)　recommendation.　However,　current　studies　have　not　unleashed　the　full　power　of　such　spatiotemporal　data,　which　either　explore　only　a　single　dimension　of　the　data　or　consider　multiple　factors　in　an　asynchronous　fashion.　In　this　article,　we　propose　a　novel　spatiotemporal　network-based　recommender　framework　(STNBR)　to　effectively　recommend　POIs　for　users.　Specifically,　we　first　establish　a　comprehensive　conceptual　model　of　spatiotemporal　data,　involving　various　essential　factors　for　POIs　recommendation.　On　top　of　the　conceptual　model,　we　design　a　series　of　meaningful　meta-paths　that　simultaneously　consider　the　time　and　location　factors　to　precisely　capture　the　semantics　of　user　behaviours.　By　profiling　users　based　on　their　embedded　meta-paths,　our　approach　can　yield　meaningful　POIs　recommendations.　We　have　evaluated　our　proposal　using　a　realistic　dataset　obtained　from　Foursquare　and　Gowalla,　the　results　of　which　show　that　our　STNBR　model　outperforms　existing　approaches.