In　the　social　image　sharing　websites　(such　as　Flickr),　users　are　allowed　to　upload　images　and　tag　them　with　tags.　Due　to　the　diversities　of　users＇　interests,　different　users　may　tag　the　same　image　with　different　tags.　Therefore,　tags　not　only　reveal　some　important　image　semantic　clues,　but　also　show　user＇s　preference,　which　can　provide　a　new　effective　solution　for　overcoming　the　semantic　gap　as　well　as　realizing　a　personalized　recommendation.　In　this　paper,　a　personalized　social　image　recommendation　method　based　on　user-image-tag　model　is　proposed.　The　main　contributions　of　our　work　are　1)　to　efficiently　make　use　of　tags,　social　image　tags　are　re-ranked　according　to　the　image　content;　2)　to　obtain　user　preference,　a　user-image-tag　model　is　constructed　with　tripartite　graph　according　to　the　correlation　among　users,　images　and　top-ranking　tags;　and　3)　a　personalized　social　recommendation　system　is　implemented　based　on　user-image-tag　model.　Experimental　results　proved　that　our　method　can　significantly　improve　the　accuracy　of　personalized　image　recommendation.