The　conventional　uniform　embeddings　lack　diversity　to　infer　users＇　interests　and　make　suboptimal　recommendations　for　users.　Fortunately,　users＇　interactions　imply　a　complex　and　hybrid　composition　of　users＇　interests　with　multiple　compatible　intents.　Therefore,　this　work　strives　to　investigate　fine-grained　interest　modeling　from　the　diversified　composition　of　interest　with　the　intent　hypothesis.　We　propose　a　cross-intent　transformer　embedding　(CITE)　for　personalized　recommendation,　which　extracts　collaborative　filtering　(CF)　signals　by　propagating　interests　within　intent　subgraphs　and　between　compatible　intents.　In　the　scenario　of　interaction　sparsity,　intent-aware　interest　propagation　employs　graph　convolution　to　ensure　interest　consistency　in　each　intent　subgraph.　It　builds　intent-aware　embeddings　with　interaction　confidences　learned　iteratively　on　each　intent　subgraph.　In　addition,　the　transformer　evaluates　inter-intent　compatibility　to　perform　cross-intent　interest　propagation.　It　updates　intent　embeddings　with　CF　signals　between　intents.　The　resulting　multiple　fine-grained　intent　embeddings　model　the　hybrid　composition　of　users＇　interests　for　personalized　recommendation.　Extensive　experiments　on　three　real-world　datasets　demonstrate　the　effectiveness　of　the　proposed　CITE　and　verify　the　active　role　of　the　compatible　intents　for　interest　modeling.