Content　curation　social　networks　(CCSNs),　such　as　Pinterest　and　Huaban,　are　interest　driven　and　content　centric.　On　CCSNs,　user　interests　are　represented　by　a　set　of　boards,　and　a　board　is　composed　of　various　pins.　A　pin　is　an　image　with　a　description.　All　entities,　such　as　users,　boards,　and　categories,　can　be　represented　as　a　set　of　pins.　Therefore,　it　is　possible　to　implement　entity　representation　and　the　corresponding　recommendations　on　a　uniform　representation　space　from　pins.　Furthermore,　lots　of　pins　are　re-pinned　from　others　and　the　pin＇s　re-pin　sequences　are　recorded　on　CCSNs.　In　this　paper,　a　framework　which　can　learn　the　multimodal　joint　representation　of　pins,　including　text　representation,　image　representation,　and　multimodal　fusion,　is　proposed.　Image　representations　are　extracted　from　a　multilabel　convolutional　neural　network.　The　multiple　labels　of　pins　are　automatically　obtained　by　the　category　distributions　in　the　re-pin　sequences,　which　benefits　from　the　network　architecture.　Text　representations　are　obtained　with　the　word2vec　tool.　Two　modalities　are　fused　with　a　multimodal　deep　Boltzmann　machine.　On　the　basis　of　the　pin　representation,　different　recommendation　tasks　are　implemented,　including　recommending　pins　or　boards　to　users,　recommending　thumbnails　to　boards,　and　recommending　categories　to　boards.　Experimental　results　on　a　dataset　from　Huaban　demonstrate　that　the　multimodal　joint　representation　of　pins　contains　the　information　of　user　interests.　Furthermore,　the　proposed　multimodal　joint　representation　outperformed　unimodal　representation　in　different　recommendation　tasks.　Experiments　were　also　performed　to　validate　the　effectiveness　of　the　proposed　recommendation　methods.