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The article proposes a variational type graph autoencoder model to address noise and data sparsity issues in event recommendation systems within Event-Based Social Networks (EBSNs). Topics discussed include the use of heterogeneous denoising techniques for reducing noise, the introduction of a heterogeneous normalization module to handle data sparsity, and the incorporation of a learnable mixture prior to model various types of contextual features.

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