Scalable Probabilistic Matrix Factorization with Graph-Based Priors

national conference on artificial intelligence, 2020.

Cited by: 1|Bibtex|Views26
Other Links: academic.microsoft.com|arxiv.org

Abstract:

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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