Scalable Probabilistic Matrix Factorization with Graph-Based Priors
national conference on artificial intelligence, 2020.
In matrix factorization, available graph side-information may not be well suited for the matrix completion problem, having edges that disagree with the latent-feature relations learnt from the incomplete data matrix. We show that removing these contested edges improves prediction accuracy and scalability. We identify the contested edges...More
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