Predicting　individual　aesthetic　preferences　holds　significant　practical　applications　and　academic　implications　for　human　society.　However,　existing　studies　mainly　focus　on　learning　and　predicting　the　commonality　of　facial　attractiveness,　with　little　attention　given　to　Personalized　Facial　Beauty　Prediction　(PFBP).　PFBP　aims　to　develop　a　machine　that　can　adapt　to　individual　aesthetic　preferences　with　only　a　few　images　rated　by　each　user.　In　this　paper,　we　formulate　this　task　from　a　meta-learning　perspective　that　each　user　corresponds　to　a　meta-task.　To　address　such　PFBP　task,　we　draw　inspiration　from　the　human　aesthetic　mechanism　that　visual　aesthetics　in　society　follows　a　Gaussian　distribution,　which　motivates　us　to　disentangle　user　preferences　into　a　commonality　and　an　individuality　part.　To　this　end,　we　propose　a　novel　MetaFBP　framework,　in　which　we　devise　a　universal　feature　extractor　to　capture　the　aesthetic　commonality　and　then　optimize　to　adapt　the　aesthetic　individuality　by　shifting　the　decision　boundary　of　the　predictor　via　a　meta-learning　mechanism.　Unlike　conventional　meta-learning　methods　that　may　struggle　with　slow　adaptation　or　overfitting　to　tiny　support　sets,　we　propose　a　novel　approach　that　optimizes　a　high-order　predictor　for　fast　adaptation.　In　order　to　validate　the　performance　of　the　proposed　method,　we　build　several　PFBP　benchmarks　by　using　existing　facial　beauty　prediction　datasets　rated　by　numerous　users.　Extensive　experiments　on　these　benchmarks　demonstrate　the　effectiveness　of　the　proposed　MetaFBP　method.　©　2023　ACM.