Eliciting　precise　user　preferences　and　establishing　a　comprehensive　user　profile　significantly　contribute　to　personalized　recommendations　from　numerous　applications.　However,　existing　methods　do　not　adequately　establish　critical　relationships　at　the　knowledge　level.　In　this　paper,　we　argue　that　precisely　processing　applications＇　user　feedback　is　essential　for　understanding　user　requirements　and　making　application　recommendations.　Therefore,　we　first　establish　an　ontological　model　of　user　feedback,　guiding　the　generation　of　a　knowledge　graph　regarding　user　reviews　and　user　ratings.　In　particular,　we　augment　the　graph　with　topics　of　each　review　and　application　in　order　to　deal　with　the　sparsity　of　user　feedback.　Moreover,　we　explore　in-depth　knowledge　from　the　graph　by　identifying　three　meaningful　meta-paths,　which　are　essential　for　calculating　user　similarity　and　thus　making　recommendations.　Specifically,　we　propose　a　feedback-based　similarity　calculation　model　FSCM,　with　the　purpose　of　predicting　applications　that　are　of　interests　of　certain　users.　We　have　evaluated　our　model　over　1386　reviews　from　a　mobile　application　store,　the　results　of　which　show　that　our　approach　can　improve　the　prediction　accuracy,　as　well　as　to　enhance　the　interpretability　of　analysis　results.