Content　curation　social　networks　(CCSN)　develop　rapidly.　Pinterest　and　Huaban　are　two　typical　CCSNs.　Recently,　there　is　active　research　on　CCSNs.　As　a　kind　of　content　based　social　network,　CCSNs　involve　not　only　the　explicit　social　relations　from　user　＂following＂,　but　also　content-based　social　relations　from　re-pin　paths　and　so　on.　In　this　paper,　we　propose　a　novel　user　representation　learning　algorithm,　Multi　perspective　User2Vec　Representation　(MUVR).　It　combines　the　two　types　of　social　relations　to　get　the　rich　user　sequences.　Then　the　representation　learning　is　implemented　by　using　the　skip-gram　algorithm.　Experimental　results　on　Huaban.com　demonstrate　that　the　proposed　algorithm　can　represent　network　well.　It　presents　more　competitive　results　in　the　followee　recommendation,　re-pinner　recommendation　and　multi-label　classification.　(C)　2017　Published　by　Elsevier　B.V.