Music　is　universally　recognized　as　an　effective　way　for　human　to　express　emotion　and　regulate　emotional　states.　But　perceived　music　emotion　is　subjective　and　much　dependent　on　culture,　environment,　and　life　experience.　Therefore,　personalized　music　recommendation　is　necessary　to　gain　user　satisfaction　and　navigate　a　listener　to　a　more　positive　emotional　state　as　well.　Existing　work　on　emotion-based　music　recommendation　and　personalized　music　recommendation　often　lack　of　considering　the　impact　of　past　life　experiences　on　music　emotion　perceiving.　We　argue　that　memories　associated　with　music　could　play　a　vital　role　in　determining　the　new　emotional　states　after　music　listening.　To　verify　our　hypothesis,　we　propose　a　personalized　music　recommendation　framework　called　MemoMusic,　which　estimates　the　new　emotional　state　of　a　listener　based　on　an　individual’s　current　emotional　state　and　possible　memory　associated　with　the　music　being　listened　to.　For　the　preliminary　experiment,　a　dataset　of　60　piano　music　was　collected　and　labelled　using　the　Valence-Arousal　model　from　three　categories　of　Classical,　Popular,　and　Yanni　music.　Experimental　results　demonstrate　that　memory　is　actually　an　important　factor　in　determining　perceived　music　emotion.　And　MemoMusic　based　on　emotion　and　memory　achieves　a　good　performance　in　terms　of　improving　a　listener’s　emotional　states.　©　2021　IEEE.