Collaborative Filtering with Graph Information: Consistency and Scalable Methods

Annual Conference on Neural Information Processing Systems, 2015.

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Abstract:

Low rank matrix completion plays a fundamental role in collaborative filtering applications, the key idea being that the variables lie in a smaller subspace than the ambient space. Often, additional information about the variables is known, and it is reasonable to assume that incorporating this information will lead to better predictions....More

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