As　the　Internet　confronts　the　multimedia　explosion,　it　becomes　urgent　to　investigate　personalized　recommendation　for　alleviating　information　overload　and　improving　users＇　experience.　Most　personalized　recommendation　approaches　pay　their　attention　to　collaborative　filtering　over　users＇　interactions,　which　suffers　greatly　from　the　highly　sparse　interactions.　In　image　recommendation,　visual　correlations　among　images　that　users　consumed　provide　a　piece　of　intrinsic　evidence　to　reveal　users＇　interests.　It　inspires　us　to　investigate　image　recommendation　over　the　dense　visual　graph　of　images　instead　of　the　sparse　user　interaction　graph.　In　this　paper,　we　propose　a　semantic　manifold　modularization-based　ranking　(MMR)　for　image　recommendation.　MMR　leverages　the　dense　visual　manifold　to　propagate　users＇　historical　records　and　infer　user-image　correlations　for　image　recommendation.　Especially,　it　constrains　interest　propagation　within　semantic　visual　compact　groups　by　manifold　modularization　to　make　a　tradeoff　between　users＇　personality　and　graph　smoothness　in　propagation.　Experimental　results　demonstrate　that　user-consumed　visual　correlations　play　actively　to　capture　users＇　interests,　and　the　proposed　MMR　can　infer　user-image　correlations　via　visual　manifold　propagation　for　image　recommendation.　(c)　2021　Elsevier　Ltd.　All　rights　reserved.