Graph　Neural　Networks　(GNNs)　have　been　successfully　used　to　learn　user　and　item　representations　for　Collaborative　Filtering　(CF)　based　recommendations　(GNN-CF).　Besides　the　main　recommendation　task　in　a　GNN-CF　model,　contrastive　learning　is　taken　as　an　auxiliary　task　to　learn　better　representations.　Both　the　main　task　and　the　auxiliary　task　face　the　noise　problem　and　the　distilling　hard　negative　problem.　However,　existing　GNN-CF　models　only　focus　on　one　of　them　and　ignore　the　other.　Aiming　to　solve　the　two　problems　in　a　unified　framework,　we　propose　a　Multi-Mixing　strategy　for　GNN-based　CF　(M2GCF).　In　the　main　task,　M2GCF　perturbs　embeddings　of　users,　items　and　negative　items　with　sample-noise　by　a　mixing　strategy.　In　the　auxiliary　task,　M2GCF　utilizes　a　contrastive　learning　mechanism　with　a　two-step　mixing　strategy　to　construct　hard　negatives.　Extensive　experiments　on　three　benchmark　datasets　demonstrate　the　effectiveness　of　the　proposed　model.　Further　experimental　analysis　shows　that　M2GCF　is　robust　against　interaction　noise　and　is　accurate　for　long-Tail　item　recommendations.　©　2023-IOS　Press.　All　rights　reserved.