<ul data-eligibleForWebStory="false"><li>The paper discusses the challenges in matrix completion for recommender systems due to data sparsity and long-tail distribution in real-world scenarios.</li><li>A new framework called Multi-Channel Hypergraph Contrastive Learning (MHCL) is proposed to address these challenges by adaptively learning hypergraph structures and capturing high-order correlations between nodes.</li><li>MHCL utilizes attention-based cross-view aggregation to jointly capture local and global collaborative relationships and encourages alignment between adjacent ratings through multi-channel cross-rating contrastive learning.</li><li>Extensive experiments on five public datasets show that MHCL outperforms current state-of-the-art approaches in rating prediction and matrix completion for recommender systems.</li></ul>

Multi-Channel Hypergraph Contrastive Learning for Matrix Completion

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