With　the　rapid　growth　of　activities　on　the　web,　large　amounts　of　interaction　data　on　multimedia　platforms　are　easily　accessible,　including　e-commerce,　music　sharing,　and　social　media.　By　discovering　various　interests　of　users,　recommender　systems　can　improve　user　satisfaction　without　accessing　overwhelming　personal　information.　Compared　to　graph-based　models,　hypergraph-based　collaborative　filtering　has　the　ability　to　model　higher-order　relations　besides　pair-wise　relations　among　users　and　items,　where　the　hypergraph　structures　are　mainly　obtained　from　specialized　data　or　external　knowledge.　However,　the　above　well-constructed　hypergraph　structures　are　often　not　readily　available　in　every　situation.　To　this　end,　we　first　propose　a　novel　framework　named　HGRec,　which　can　enhance　recommendation　via　automatic　hypergraph　generation.　By　exploiting　the　clustering　mechanism　based　on　the　user/item　similarity,　we　group　users　and　items　without　additional　knowledge　for　hypergraph　structure　learning　and　design　a　cross-view　recommendation　module　to　alleviate　the　combinatorial　gaps　between　the　representations　of　the　local　ordinary　graph　and　the　global　hypergraph.　Furthermore,　we　devise　a　sparse　optimization　strategy　to　ensure　the　effectiveness　of　hypergraph　structures,　where　a　novel　integration　of　the　l(　2,1)-norm　and　optimal　transport　framework　is　designed　for　hypergraph　generation.　We　term　the　model　HGRec　with　sparse　optimization　strategy　as　HGRec++.　Extensive　experiments　on　public　multi-domain　datasets　demonstrate　the　superiority　brought　by　our　HGRec++,　which　gains　average　8.1%　and　9.8%　improvement　over　state-of-the-art　baselines　regarding　Recall　and　NDCG　metrics,　respectively.