Recommender　systems　filter　information　to　meet　users＇　personalized　interests　actively.　Existing　graph　-based　models　typically　extract　users＇　interests　from　a　heterogeneous　interaction　graph.　They　do　not　distinguish　learning　between　users　and　items,　ignoring　the　heterogeneous　property.　In　addition,　the　interaction　sparsity　and　long　-tail　bias　issues　still　limit　the　recommendation　performance　significantly.　Fortunately,　hidden　homogeneous　correlations　that　have　a　considerable　volume　can　entangle　abundant　CF　signals.　In　this　paper,　we　propose　a　light　dual　hypergraph　convolution　(LDHC)　for　collaborative　filtering,　which　designs　a　hypergraph　to　involve　heterogeneous　and　homogeneous　correlations　with　more　CF　signals　confronting　the　challenges.　Over　the　integrated　hypergraph,　a　two　-level　interest　propagation　is　performed　within　the　heterogeneous　interaction　graph　and　between　the　homogeneous　user/item　graphs　to　model　users＇　interests,　where　learning　on　users　and　items　is　distinguished　and　collaborated　by　the　homogeneous　propagation.　Specifically,　hypergraph　convolution　is　lightened　by　removing　unnecessary　parameters　to　propagate　users＇　interests.　Extensive　experiments　on　publicly　available　datasets　demonstrate　that　the　proposed　LDHC　outperforms　the　state-of-the-art　baselines.