Recommender　systems　help　users　filter　items　they　may　be　interested　in　from　massive　multimedia　content　to　alleviate　information　overload.　Collaborative　filtering-based　models　perform　recommendation　relying　on　users＇　historical　interactions,　which　meets　great　difficulty　in　modeling　users＇　interests　with　extremely　sparse　interactions.　Fortunately,　the　rich　semantics　hidden　in　items　may　be　promising　in　helping　to　describing　users＇　interests.　In　this　work,　we　explore　the　semantic　correlations　between　items　on　modeling　users＇　interests　and　propose　knowledge-aware　multispace　embedding　learning　(KMEL)　for　personalized　recommendation.　KMEL　attempts　to　model　users＇　interests　across　semantic　structures　to　leverage　valuable　knowledge.　High-order　semantic　collaborative　signals　are　extracted　in　multiple　independent　semantic　spaces　and　aggregated　to　describe　users＇　interests　in　each　specific　semantic.　The　semantic　embeddings　are　adaptively　integrated　with　a　target-aware　attention　mechanism　to　learn　cross-space　multisemantic　embeddings　for　users　and　items,　which　are　fed　to　the　subsequent　pairwise　interaction　layer　for　personalized　recommendation.　Experiments　on　real-world　datasets　demonstrate　the　effectiveness　of　the　proposed　KMEL　model.