With　the　rapid　evolution　of　social　networks,　the　increasing　user　intention　gap　and　visual　semantic　gap　both　bring　great　challenge　for　users　to　access　satisfied　contents.　It　becomes　promising　to　investigate　users＇　customized　multimedia　recommendation.　In　this　paper,　we　propose　cross-modal　collaborative　manifold　propagation　(　CMP)　for　image　recommendation.　CMP　leverages　users＇　interest　distribution　to　propagate　images＇　user　records,　which　lets　users　know　the　trend　from　others　and　produces　interest-aware　image　candidates　upon　users＇　interests.　Visual　distribution　is　investigated　simultaneously　to　propagate　users＇　visual　records　along　dense　semantic　visual　manifold.　Visual　manifold　propagation　helps　to　estimate　semantic　accurate　user-image　correlations　for　the　candidate　images　in　recommendation　ranking.　Experimental　performance　demonstrate　the　collaborative　user-image　inferring　ability　of　CMP　with　effective　user　interest　manifold　propagation　and　semantic　visual　manifold　propagation　in　personalized　image　recommendation.