Previous　works　build　interest　learning　via　mining　deeply　on　interactions.　However,　the　interactions　come　incomplete　and　insufficient　to　support　interest　modeling,　even　bringing　severe　bias　into　recommendations.　To　address　the　interaction　sparsity　and　the　consequent　bias　challenges,　we　propose　a　graph　contrastive　learning　on　complementary　embedding　(GCCE),　which　introduces　negative　interests　to　assist　positive　interests　of　interactions　for　interest　modeling.　To　embed　interest,　we　design　a　perturbed　graph　convolution　by　preventing　embedding　distribution　from　bias.　Since　negative　samples　are　not　available　in　the　general　scenario　of　implicit　feedback,　we　elaborate　a　complementary　embedding　generation　to　depict　users＇　negative　interests.　Finally,　we　develop　a　new　contrastive　task　to　contrastively　learn　from　the　positive　and　negative　interests　to　promote　recommendation.　We　validate　the　effectiveness　of　GCCE　on　two　real　datasets,　where　it　outperforms　the　state-of-the-art　models　for　recommendation.