The　information　era　brings　both　opportunities　and　challenges　to　information　services.　Confronting　information　overload,　recommendation　technology　is　dedicated　to　filtering　personalized　content　to　meet　users＇　requirements.　The　extremely　sparse　interaction　records　and　their　imbalanced　distribution　become　a　big　obstacle　to　building　a　high-quality　recommendation　model.　In　this　article,　we　propose　a　swarm　self-supervised　hypergraph　embedding　(SHE)　model　to　predict　users＇　interests　by　hypergraph　convolution　and　self-supervised　discrimination.　SHE　builds　a　hypergraph　with　multiple　interest　clues　to　alleviate　the　interaction　sparsity　issue　and　performs　interest　propagation　to　embed　CF　signals　in　hybrid　learning　on　the　hypergraph.　It　follows　an　auxiliary　local　view　by　similar　hypergraph　construction　and　interest　propagation　to　restrain　unnecessary　propagation　between　user　swarms.　Besides,　interest　contrast　further　inserts　self-discrimination　to　deal　with　long-tail　bias　issue　and　enhance　interest　modeling,　which　aid　recommendation　by　a　multi-task　learning　optimization.　Experiments　on　public　datasets　show　that　the　proposed　SHE　outperforms　the　state-of-the-art　models　demonstrating　the　effectiveness　of　hypergraph-based　interest　propagation　and　swarm-aware　interest　contrast　to　enhance　embedding　for　recommendation.