With　the　recent　popularity　of　social　network　services,　a　significant　volume　of　heterogeneous　social　media　data　is　generated　by　users,　in　the　form　of　texts,　photos,　videos　and　collections　of　points　of　interest,　etc.　Such　social　media　data　provides　users　with　rich　resources　for　exploring　content,　such　as　looking　for　an　interesting　video　or　a　favorite　point　of　interest.　However,　the　rapid　growth　of　social　media　causes　difficulties　for　users　to　efficiently　retrieve　their　desired　media　items.　Fortunately,　users＇　digital　footprints　on　social　networks　such　as　comments　massively　reflect　individual＇s　fine-grained　preference　on　media　items,　that　is,　preference　on　different　aspects　of　the　media　content,　which　can　then　be　used　for　personalized　social　media　search.　In　this　article,　we　propose　SESAME,　a　fine-grained　preference-aware　social　media　search　framework　leveraging　user　digital　footprints　on　social　networks.　First,　we　collect　users＇　direct　feedback　on　media　content　from　their　social　networks.　Second,　we　extract　users＇　sentiment　about　the　media　content　and　the　associated　keywords　from　their　comments　to　characterize　their　fine-grained　preference.　Third,　we　propose　a　parallel　multituple　based　ranking　tensor　factorization　algorithm　to　perform　the　personalized　media　item　ranking　by　incorporating　two　unique　features,　viz.,　integrating　an　enhanced　bootstrap　sampling　method　by　considering　user　activeness　and　adopting　stochastic　gradient　descent　parallelization　techniques.　We　experimentally　evaluate　the　SESAME　framework　using　two　datasets　collected　from　Foursquare　and　YouTube,　respectively.　The　results　show　that　SESAME　can　subtly　capture　user　preference　on　social　media　items　and　consistently　outperform　baseline　approaches　by　achieving　better　personalized　ranking　quality.