Most　traditional　recommendation　systems　are　based　on　homogeneous　networks.　However,　a　large　number　of　real-world　graphs　or　networks　are　inherently　heterogeneous,　involving　a　diversity　of　node　types　and　relationship　types.　Although　the　existing　recommendation　approaches　based　on　heterogeneous　information　network　have　achieved　performance　improvement,　while　they　still　face　the　following　problems.　On　one　hand,　existing　models　usually　define　multiple　meta-paths　in　heterogeneous　graphs　to　capture　the　composite　relations　and　guide　neighbor　selection.　However,　these　models　usually　discard　intermediate　nodes　along　the　meta-path　and　cannot　fully　mine　the　interaction　information　of　heterogeneous　graphs.　On　the　one　hand,　they　do　not　take　into　account　the　time　sequence　between　user　and　items.　To　address　these　two　limitations,　we　propose　a　new　model　named　Time-aware　Interaction　networks　for　Recommendation　based　on　Heterogeneous　information　network　(TIH4Rec)　to　improve　performance.　Specifically,　TIH4Rec　employs　two　major　components,　i.e.,　an　interaction　module　that　aggregates　semantic　information　inter-meta-path　and　a　time-aware　module　that　incorporates　time　information.　Experiments　on　two　publicly　heterogeneous　graph　datasets　demonstrate　the　performance　gains　of　TIH4Rec　comparing　with　baselines.　©　2021　IEEE.