MemoMusic　3.0　enhances　personalized　music　recommendation　by　considering　the　music　listening　context,　and　improves　music　generation　by　introducing　music　theory.　One　observation　is　that　the　context　of　music　listening　would　affect　the　emotional　states　of　listeners,　positively　or　negatively.　The　other　is　that　better　music　can　be　generated　by　introducing　some　music　theory　knowledge.　We　propose　a　Transformer-based　music　generation　framework,　which　is　trained　into　three　models　for　Classic,　Pop,　and　Yanni　music　respectively.　The　dominant　melody　of　a　music　with　expected　Valence　and　Arousal　values　is　used　as　a　sample　sequence　to　the　model,　and　its　output　is　adjusted　according　to　music　theory.　Experimental　results　demonstrate　that　MemoMusic　3.0　performs　better　at　improving　the　emotional　states　of　listeners　and　achieves　better　user　satisfaction.　©　2023　IEEE.