The　popularity　of　online　social　curation　networks　takes　benefits　from　its　convenience　to　retrieve,　collect,　sort　and　share　multimedia　contents　among　users.　With　increasing　content　and　user　intent　gap,　effective　recommendation　becomes　highly　desirable　for　its　further　development.　In　this　paper,　we　propose　a　content-based　bipartite　graph　for　image　recommendation　in　social　curation　networks.　Bipartite　graph　employs　given　sparse　user-image　interactions　to　infer　user-image　correlation　for　recommendation.　Beside　given　user-image　interactions,　the　user　interacted　visual　content　also　reveals　valuable　user　preferences.　Visual　content　is　embedded　into　the　bipartite　graph　to　extend　the　correlation　density　and　the　recommendation　scope　simultaneously.　Furthermore,　the　content　similarity　is　employed　for　recommendation　reranking　to　improve　the　visual　quality　of　recommended　images.　Experimental　results　demonstrate　that　the　proposed　method　enhances　the　recommendation　ability　of　the　bipartite　graph　effectively.