Online　movies＇　recommender　systems　aim　to　address　the　information　explosion　of　movies　and　make　the　personalized　recommendation　for　users.　Recently,　knowledge　graphs　have　been　proven　to　be　highly　effective　to　recommender　systems,　because　they　are　able　to　fuse　various　recommendation　models　and　can　handle　the　issues　of　data　sparsity　and　cold　start　to　improve　recommendation　performance.　However,　less　consideration　is　given　to　the　information　about　the　user＇s　properties　than　the　item＇s　properties　in　the　existing　knowledge　graph　recommendation　methods,　which　leads　to　some　limitations　in　the　recommendation　results.　In　this　paper,　we　propose　HI2Rec,　which　integrates　multiple　information　to　learn　the　user＇s　and　item＇s　vector　representations　for　top-N　recommendation　to　address　the　above-mentioned　issues.　We　extract　the　movie-related　information　from　the　Linked　Open　Data　and　then　leverage　the　knowledge　representation　learning　approach　to　embed　this　information　as　well　as　real-world　datasets＇　information　of　recommender　systems　to　a　unified　vector　space.　These　vector　representations　are　further　calculated　to　generate　a　preliminary　recommendation　list.　Finally,　we　utilize　a　collaborative　filter　approach　to　generate　a　precision　recommendation　list.　The　experimental　results　on　the　real-world　datasets　demonstrate　that　HI2Rec　gives　substantial　performance　improvements　against　the　state-of-the-art　recommendation　models.