Photo　recommendation　in　photo-sharing　social　networks　like　Flickr　is　an　important　problem.　Collaborative　filtering　is　very　popular,　which　assumes　each　item　has　the　same　weight　for　recommendation.　In　practice　some　items　are　representatives　for　a　class　of　items　and　therefore　are　more　important　for　recommendation.　In　this　paper,　we　model　the　importance　for　items　by　examining　sentiment　from　the　general　public　towards　items.　Specifically　we　propose　a　model　using　the　temporal　dynamic　user　＇favor＇　information　to　infer　photo　importance　on　Flickr.　It　is　further　combined　with　local　community　user　ratings　to　improve　the　Probabilistic　Matrix　Factorization　(PMF)　framework　for　photo　recommendation.　Experiment　results　show　the　effectiveness　of　the　proposed　approach.