Point　of　interest　(POI)　recommendation　is　a　fundamental　task　in　location-based　social　networks　(LBSN).　The　increasing　proliferation　of　LBSNs　brings　about　considerable　amounts　of　user-generated　check-in　data.　Such　data　can　significantly　contribute　to　understanding　user　behaviors,　based　on　which　personalized　recommendations　can　be　efficiently　derived.　Spatial　and　temporal　effects　are　crucial　factors　in　the　user＇s　decision-making　for　choosing　a　POI　to　visit.　Most　existing　methods　treat　them　as　two　independent　features　and　cannot　accurately　capture　users＇　interests.　We　argue　that　spatial　and　temporal　effects　should　be　analyzed　simultaneously　in　POI　recommendations.　To　this　end,　we　propose　a　S　patioT　emporal　heterogeneous　information　Network　(HIN)-based　PO　I　RE　commendation　model　(STORE)　to　model　various　heterogeneous　context　features,　e.g.,　the　joint　spatiotemporal　effects,　types　of　POI,　and　social　relations.　Specifically,　we　defined　the　spatiotemporal　effects　entity　(St)　in　HIN　to　model　the　joint　spatiotemporal　effects.　Instead　of　modeling　the　traditional　two-way　interaction　,　we　further　design　a　four-way　neural　interaction　model　.　In　this　way,　our　model　can　effectively　mine　and　extract　useful　information　from　the　meta-path-based　context　and　spatiotemporal　effects,　thereby　improving　recommendation　performance.　We　conduct　extensive　experiments　on　two　real-world　datasets,　and　the　results　demonstrate　that　the　STORE　model　outperforms　the　best　baseline　by　about　12%　in　NDCG@5　and　11%　in　Rec@5.