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.
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