Multimedia　recommender　systems　(MRS)　become　prevalent　due　to　their　rich　multimodal　data　(e.g.,　visual　and　textual　content).　Recent　advancements　have　leveraged　Graph　Neural　Networks　(GNNs)　to　integrate　these　data,　they　often　fall　short　in　capturing　the　complex　high-order　relations　within　multimodal　data,　but　readily　hypergraph　structures　are　not　always　available.　To　this　end,　we　introduce　the　HMRec　framework,　a　novel　approach　in　Heterogeneous　Hypergraph　Structure　Learning　tailored　for　MRS.　Specifically,　we　formulate　the　construction　of　a　heterogeneous　hypergraph　as　determining　item　associations　across　modalities,　and　introduce　an　adaptive　hypergraph　convolution　mechanism　for　differentially　weighting　multimodal　hyperedges.　Furthermore,　we　propose　an　enhanced　multimedia　recommendation　module,　which　introduces　a　contrastive　fusion　mechanism　to　effectively　integrate　graph-view,　hypergraph-view,　and　ID-specific　embeddings.　Extensive　experiments　on　real-world　multimodal　datasets　show　the　superiority　of　our　proposed　HMRec　framework　in　offering　great　potential　for　multimedia　recommendations　over　the　state-of-the-art　baselines　regarding　the　Recall　and　NDCG　metrics.　©　2024　IEEE.