Recommender　systems　have　become　an　indispensable　tool　in　today＇s　digital　age,　significantly　enhancing　user　engagement　on　various　online　platforms　by　curating　personalized　item　recommendations　tailored　to　individual　preferences.　While　the　field　has　long　been　dominated　by　the　collaborative　filtering　technique,　which　primarily　leverages　user-item　interaction　data,　it　often　falls　short　in　encapsulating　the　rich　contextual　intricacies　and　evolving　dynamics　inherent　to　these　interactions.　Recognizing　this　limitation,　our　research　introduces　the　contextual　semantic　interaction　graph　embedding　(CSI-GE)　method.　This　advanced　model　incorporates　a　dynamic　hop　window　within　a　multilayer　graph　convolutional　network,　ensuring　a　comprehensive　extraction　of　both　immediate　and　evolving　contextual　features.　By　amalgamating　self-supervised　contrastive　learning,　we　achieve　a　refinement　of　user　and　item　embeddings.　Furthermore,　our　innovative　variance-invariance-covariance　(VIC)　regularization-based　loss　function　fortifies　the　robustness　of　these　embeddings.　Through　rigorous　testing,　CSI-GE　consistently　outperformed　contemporary　methods,　underscoring　its　superior　accuracy　and　stability.