Collaborative Filtering with the Trace Norm: Learning, Bounding, and Transducing

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Introduction to Online Convex Optimization (2016, background): “The matrix completion model has been extremely popular since its inception in the context of recommendation systems (103; 88; 94; 70; 26; 101).”
Excess risk bounds for multitask learning with trace norm regularization (2012, background): “A lot of important work on trace norm regularization concerns matrix completion, where a matrix is only partially observed and approximated (or under certain assumptions even reconstructed) by a matrix of small trace norm (see e.g. [11], [25] and references therein).”
Differentially Private Matrix Completion, Revisited (2017, methodology): “The generalization guarantee in Corollary 3.1 is for uniformly random Ω, but using results of [40], it is straightforward to extend our results to any general i.i.d. distribution over Ω.”
Online linear optimization with the log-determinant regularizer (2017, background): “In this perspective, the policy class is formulated as a restricted set of matrices, say, the set of matrices of bounded trace norm, which is commonly used in collaborative filtering [7, 8, 9, 10].”