Recently,　online　social　curation　networks　attract　lots　of　users　due　to　its　convenience　to　retrieve,　collect,　sort　and　share　multimedia　content　with　each　other.　And　high　quality　recommendation　on　social　curation　networks　becomes　urgent　in　current　complex　information　environment.　In　this　paper,　we　proposed　a　content-based　bipartite　graph　algorithm　for　social　curation　network　recommendation.　Bipartite　graph　employs　relationships　between　users　and　items　to　infer　user-item　association　for　recommendation.　Beyond　the　traditional　bipartite　graph,　we　introduce　the　content　of　items　into　bipartite　graph　to　extend　the　recommendation　scope　and　improve　its　recommendation　diversity　simultaneously.　Furthermore,　content　similarity　is　employed　for　recommendation　reranking　to　improve　visual　quality　of　recommended　images.　Experimental　results　demonstrate　that　the　proposed　method　enhance　the　recommendation　ability　of　bipartite　graph　effectively　in　diversity　and　visual　quality.　©　Springer　Nature　Singapore　Pte　Ltd.　2018.