With　the　rapid　growth　of　multimedia-sharing　platforms　(e.g.　Twitter　and　TikTok),　multimedia　recommender　systems　have　become　fundamental　for　helping　users　alleviate　information　overload　and　discover　items　of　interest.　Existing　multimedia　recommendation　methods　often　incorporate　various　auxiliary　modalities　(e.g.,　visual,　textual,　and　acoustic)　to　describe　item　characteristics　and　improve　task　performance.　However,　these　methods　usually　assume　that　each　item　is　associated　with　complete　modalities,　ignoring　the　prevalence　of　missing　modality　issues　in　real-world　scenarios.　To　deal　with　the　challenge　of　missing　modalities,　in　this　paper,　we　propose　a　novel　framework　of　Contrastive　Intra-　and　Inter-Modality　Generation　(CI(2)MG)　for　enhancing　incomplete　multimedia　recommendation.　We　first　develop　a　contrastive　intra-　and　inter-modality　generation　module　for　the　missing　modalities,　where　the　intra-modality　representation　is　updated　through　clustering-based　hypergraph　convolution　and　inter-modality　representation　is　obtained　by　optimal　transport　between　different　modalities.　To　tackle　the　challenge　of　insufficient　and　incomplete　supervision　labels　during　intra-　and　inter-modality　generation,　a　modality-aware　contrastive　learning　paradigm　is　introduced　based　on　an　augmentation　between　the　intra-modality　view　and　inter-modality　view.　Furthermore,　to　learn　task-related　representations　from　the　generative　modalities　and　further　improve　the　performance　of　recommendation,　we　design　an　enhanced　multimedia　recommendation　module　to　alleviate　the　influences　driven　by　task-irrelevant　noise.　Extensive　experiments　on　real-world　datasets　show　the　superiority　of　our　proposed　CI(2)MG　framework　in　offering　great　potential　for　personalized　multimedia　recommendation　over　the　state-of-the-art　baselines　regarding　Recall,　NDCG,　and　Precision　metrics.